United States
Environmental Protection
Agency
Office of Research and
Development
Washington, DC 20460
March 1993
Conference on the Risk
Assessment Paradigm
After Ten Years:
Policy and Practice Then,
Now, and in the Future
April 5-8, 1993
Hope Hotel and Conference Center
Dayton, Ohio
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EPA/630/R-93/039
March 1993
CONFERENCE ON THE RISK ASSESSMENT PARADIGM AFTER TEN YEARS;
POLICY AND PRACTICE THEN, NOW, AND IN THE FUTURE
BIOGRAPHIES AND PAPERS
SPONSORS
Toxicology Division, Occupational and Environmental Health
Directorate, Armstrong Laboratory
Naval Medical Research Institute Detachment (Toxicology)
Army Biomedical Research and Development Laboratory
The U.S. Environmentcil Protection Agency, Environmental Criteria
and Assessment Office
In Cooperation with
The National Research Council Committee on Toxicology
Printed on Recycled Paper
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BROTHER, TIMES ARE TOUGH; CAN YOU PARADIGM?
Dr. Donald G. Barnes
Staff Director
Science Advisory Board of the USEPA
OUTLINE OF THE TALK
I. Introduction
A. The Paradigm
1. Risk Assessment
a. "Is this stuff toxic?" - Hazard identification
b. "How toxic is this stuff?" - Dose/Response assessment
c. "Who is exposed to this stuff, to how much, how often, and for
how long?" - Exposure assessment
d. "So what?" - Risk characterization
2. Risk Management: "So what are you going to do about it?
B. The Talk
1. Impact and strengths of the paradigm
2. Strains and weaknesses of the paradigm
3. Where do we go from here?
The Impact and Strengths of the MAS Approach
A. Succeeded in nailing a lot of - but not all of - Jello to the wall
1. Common, somewhat demystified lexicon
a. For anointed practitioners
b. For laypeople
2. Conceptual separation of church (RA) and State (RM)
3. Broad application to different situations
• 4. Distinguishes between areas of fact and "faith," shining a research light
into the darkness
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B. Evidence of its Utility
1. Wide adoption of the concepts; e.g.,
a. EPA guidelines
b. IRIS
c. Other agencies; e.g., HHS, where an official once denigrated
risk assessment (not totally without basis) as being "as accurate
as a five-year weather forecast"
d. Innumerable articles, books and conferences
2. Structured, disciplined decision-making that can distinguish between
RA and RM, relieving the burden on the scientist while increasing the
burden on the risk manager
III. Strains and Weaknesses of the MAS Approach
A. Total separation of RA and RM is not possible - or even desirable in many
instances
B. As used, the paradigm favors a reductionist - single chemical (stressor) -
approach; i.e., prejudices us against addressing mixtures in a holistic manner
C. Current approach does not address adversity of effect
D. As used, there is ambiguity about whether hazard identification should relate
to "exposure under any conditions" or "exposure likely to be encountered in
the environment"
E. Ecological paradigm is purported to be different
IV. Where "do we go from here?
A. Dynamic tension in the issues in III probably precludes their final resolution
in the short term; however, they should be addressed at the technical and
• policy levels (Review each of the points and give recommendations)
B. The long-awaited NAS "update"
V. Conclusions and Recommendations
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DISCUSSION OF SELECTED ISSUES
III. Strains and Weaknesses of the NAS Approach
A. Total separation of RA and RM is not possible - or even desirable in many
instances
Some degree of interaction between RA and RM is essential if the RA answers are
going to address - let alone satisfy - the RM questions. In some instances, for example,
a qualitative answer will suffice; e.g., "Could the pollutant run off into the stream and
.bioaccumulate in aquatic organisms?" In other instances, a more detailed answer is
neecjecj; e.g., "What are the remedial options that would prevent runoff of the pollutant to
such an extent that bioaccumulation would be maintained at levels below a 10~5 risk level
to the sportsfishing population?"
In fact, the NAS considered the option of separating the RA operations totally from
the RM operations; e.g., establish a separate agency to conduct such analyses. To their
credit, the NAS panel rejected this option as being infeasible, while spotlighting the
.importance of separating RA and RM functions within'a single agency.
In finding the proper balance, there will - and should - be a continual tension
between the need for good communication between the customer (RM) and supplier
(RA). ,
B. As used, the paradigm favors a reductionist - single chemical (stressor) -
approach; i.e., prejudices us against addressing mixtures in a holistic manner
After an initial confusing and often tense several years after the NAS (1983) report,
risk assessors and risk managers have mgje or less sorted their respective roles and
responsibilities under the paradigm. This confusion, tension and resolution has proved
very useful for environmental protection. It has, for example, allowed the U.S. EPA to
form a Risk Assessment Forum, a Reference Dose (RfD)/Reference Concentration (RfC)
Work Group and a Carcinogen Risk Assessment Verification Endeavor (CRAVE) Work
Group. These three groups focus on risRassessment guidelines, methods, and chemical-
specific evaluations on an intra-agency basis. Evaluations and recommendations of these
groups are then used by risk, managers of different program offices in their decision
process.
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Risk assessment and risk management have been practiced most often on a
chemical-by-chemical basis. One might say that the paradigm even favors such an
analysis since initial steps in the paradigm (i.e., hazard Identification, exposure
assessment) operate most readily on a single chemical basis. Unfortunately, our
environment seldom offers us or our ecosystems exposures to single chemicals. Future
shins in the paradigm must stress this reality, and guide both assessors and managers
into credible approaches to this mixtures problem.
C. Current approach does not address adversity of effect .
A perennial chestnut, the issue of adversity of effect - e.g., "Which is worse:
cancer or development effects?" - has withstood all attempts at definitive resolution by
the Agency, the MAS, and anyone else for that matter. At bottom, the question of the
relative concern of leukemia vs. missing limbs or reproductive effects vs. stratospheric
ozone depletion appears to be a "value judgment" to be made somewhere else other than
\n the RA arena While such a statement may be true, it reflects a limitation in the ability
of RA to answer the RM question: "What are you going to do about these two risks?"
There is increasing recognition that resources to address environmental problems
are limited Consequently, trade-offs have to be made in many cases, including those in
which a variety of MAS risks estimated via the MAS paradigm are generally equaL In
order to reach decisions in such cases, therefore, something beyond the MAS paradigm
is needed.
D As used, there is ambiguity about whether hazard identification should relate
to "exposure under any conditions" or "exposure likely to be encountered in
the environment"
As most often used under the paradigm, hazard identification focuses on
laboratory investigation of toxic effect, whether looking at experimental animals as
surrogates for humans, or at single species as surrogates for a community or ecosystem.
Epidemiological or field investigation also .serves a very useful role in identifying hazards,
especially when effects can be clearly ascribed to the stressor. Scientists restrict such
laboratory, epidemiological or field investigation to limited exposures. By necessity, these
exposures are then generalized to environmental situations.
One purpose for such restriction is to obtain unequivocal results. Since hazard
identification often starts a more comprehensive risk assessment resulting in a risk
management decision, .unequivocal results are highly valued. Unfortunately, such
unequivocal hazard identification sometimes (often?) has limited relevance to an
environmental exposure, since environmental exposures are often to mixtures or forms
of the chemical that were not tested.
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The current paradigm supports this dilemma. Improvements in the paradigm
should discuss what, if anything, can be done about this.
E. Ecological paradigm is purported to be different
Recently, the Agency issued an "Ecological Risk Assessment Framework"
document containing a paradigm for eco-RA that is somewhat different from that
proposed by the NAS a decade ago. The reasons for these differences are several; e.g.,
• We know more about RA than we did 10 years ago
• The ecological problems are qualitatively different from the health problems -
that were the focus of the original NAS concerns
• The ecological community wanted to leave their distinctive mark along the RA
trail •;• .
In any event, there should be a single RA paradigm that is sufficiently broad to
encompass both health and ecological concerns. Currently, there is the danger that
eco-RAers and health-RAers will evolve in different - and possibly opposing - directions.
The views expressed in this paper are those of the author and do not necessarily
represent thfee of EPA or the Science Advisory Board.
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BIOSKETCH: LAWRENCE W. BARNTHOUSE
Dr Barnthouse has been a staff member in the Environmental
Sciences Division (BSD) at Oak Ridge National Laboratory (ORNL) for
16 years, and has been leader of ESD's Environmental Risk Group
since 1983. He received his A.B. Degree in Biology from Kenyon
College in 1968 and his Ph.D. in Biology from the University of
Chicago in 1976. He has led or participated in a wide variety of
ecological research and assessment projects involving common themes
of (1) extrapolating from laboratory to field, (2) modeling of
population and ecosystem responses to environmental stress, and (3)
relating scientific information to regulatory needs. Major
problems addressed have included impacts of power plant cooling
systems on estuarine fish populations and communities; ecological
risks of synthetic fossil fuel technologies; and evaluation of
remedial action priorities at contaminated sites.
Dr. Barnthouse also serves as Deputy Director of the ORNL Center
for Risk Management. He is a member of the National Research
Council's Committee on Risk Assessment Methodologies, where he
chairs the Ecological Risk Topic Group. He has written numerous
publications on ecological risk assessment and was named Marietta
Energy Systems Author of the Year for 1991. In November 1992 he
became Hazard Assessment Editor of Environmental Toxicology and
Chemistry.
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Issues in Ecological Risk Assessment: the CRAM Perspective
Lawrence W. Barnthouse
Environmental Sciences Division
Oak Ridge National Laboratory
Box 2008, Oak Ridge, Tennessee1
1 Managed by Martin Marietta Energy Systems, Incorporated under Contract DE-AC05-84OR214QO with
the U.S. Department of Energy
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Abstract
In 1989, a Committee on Risk Assessment Methodology was convened by the National Research
Council'to identify and investigate important scientific issues in risk assessment. One of the first issues
considered by the committee was the development of a conceptual framework for ecological risk
assessment, defined as the characterization of (he adverse ecological effects of environmental exposures to
hazards imposed by human activities.. "Adverse ecological effects" include all biological and
nonbiological environmental changes that society perceives as undesirable. The committee's opinion was
that a general framework is needed to define the relationship of ecological risk assessment to
environmental management and to facilitate the development of uniform technical guidelines. The
framework for human health risk assessment proposed by the NRC in 1983 was adopted as a starting
point for discussion.
CRAM concluded that, although ecological risk assessment and human health risk assessment differ
substantially in terms of scintific disciplines and technical problems, the underlying decision process is the
same for both. CRAM therefore recommended that the 1983 risk assessment framework be modified to
' accomodate both human health and ecological risk assessment. CRAM defined an integrated
health/ecological risk assessment framework consisting of the four components Hazard Identification,
Exposure Assessment, Exposure-response Assessment, and Risk Characterization. CRAM further
provided recomendations on the scope of issues to be addressed in ecological risk assessment, critical
research needs, and mechanisms for providing more detailed guidance on the scientific content of
ecological risk assessments.
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Introduction
In 1983 the National Research Council's Committee on the Institutional Means for Assessment of
Risks to Public Health published a landmark report on human health risk assessment. The report, Risk
Assessment in the Federal Government: Managing the Process [I], proposed a conceptual framework
for risk assessment that incorporates research, risk assessment, and risk management. Risk assessment
was defined as "...the characterization of the potential adverse health effects of human exposures to
environmental hazards." The report proposed a conceputal scheme for risk assessment consisting of four
components: hazard identification, dose-response assessment, exposure assessment, and risk
characterization. The report did not, however, include in-depth discussion of scientific issues' in health
risk assessment. The 1983 committee's objectives were limited to addressing institutional and procedural
issues: whether the analytic process of risk assessment should be cleanly separated from the regulatory
process of risk management, whether single organization could be designated to perform risk assessments
for all regulatory agencies, and whether uniform risk assessment guidelines could be developed for use by
all regulatory agencies. Detailed development of technical guidelines was left to the agencies themselves.
In 1989, a new Committee on Risk Assessment Methodology (CRAM) was convened within the
Board on Environmental Studies and Toxicology of the National Research Council's Commission on Life
Sciences to identify and investigate important scientific issues in risk assessment. The committee was
asked to consider changes in the scientific foundation of risk assessment that have occurred since the 1983
report and to consider applications of risk assessment to non-cancer endpoints. The first three issues
considered by CRAM were (1) the use of the Maximum Tolerated Dose in animal bioassays, (2) the use
of the two-stage model of carcinogenisis, and (3) the development of a concetpual framework for
ecological risk assessment The committee has recently issued a report on these three issues [2]. In
addition to describing an integrated framework for human health and ecological risk assessment, CRAM's
report discusses the scope of applicability of ecological risk assessment and identifies major categories of
scientific uncertainty for which additional research is needed. The purpose of this paper is to briefly
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describe the framework recommended by the Committee and compare it to EPA's recently-published
Framework for Ecological Risk Assessment [3, 4].
F.rn1n?ical risk assessment was defined by CRAM as the characterization of the adverse
ecological effects of environmental exposures to hazards imposed by human activities.
"Adverse ecological effects" include all biological and nonbiological environmental changes that society
perceives as undesirable. "Hazards" include both unintentional hazards such as pollution and soil erosion
and deliberate management activities such as forestry and fishing that are often hazardous either to the
managed resource itself or to other components of the environment. The committee's opinion was that a
general framework analogous to the 1983 human health risk assessment framework is needed to define the
relationship of ecological,risk assessment to environmental management and to facilitate the development
of uniform technical guidelines. A framework for ecological risk assessment could, for example, be used
to:
Evaluate the consistency and adequacy of individual assessments,
Compare assessments for related environmental problems,
Identify explicitly the connections between risk assessment and risk management,
and
Identify environmental research topics and data needs common to many ecological
risk assessment problems.
Like the health risk assessment framework, an ecological risk assessment framework would define the
boundaries between risk assessment and risk management and identify general categories of scientific
information relevant to risk assessment, but would not provided specific technical guidance.
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The committee chose to investigate the feasibility issue by conducting a workshop in which six
case studies representing different types of current assessments would be examined with respect to their
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consistency with a common framework. The six case studies included:
• Assessing the effects of tributyltin on Chesapeake Bay shellfish populations,
• Testing agricultural chemicals for ecological effects
• Predicting the fate and effects of polychlorinated biphenyls,
• Assessing responses of populations to habitat change,
• Regulating species introductions, and
• Harvesting the Georges Bank multispecies fishery.
A Workshop on Ecological Risk Assessment was held on February 26-March 1,1991, at Air lie
House, Warrenton, Virginia. In addition to presentation and discussion of the case study papers, the
workshop included breakout sessions to discuss conceptual and technical aspects of ecological risk
assessment. A summary of the workshop presentations and dicsussion is included as an appendix to the
CRAM report [2]; three of the case study papers have been independently published [5, 6, 7]. A
general consensus emerged at the workshop that an ecological version of the 1983 framework is desirable
and feasible, but no specific endorsement of a particular framework was sought or obtained. On
reviewing the written materials produced at the workshop, the committee concluded that the 1983 human
health framework could be expanded to accomodate both human health and ecological risk assessment.
For general applicability to ecological assessments, the 1983 scheme requires augmentation to address
some of the interfaces between science and management, primarily because of the need to focus oh
appropriate questions relevant to applicable environmental law and policy under different circumstances.
Specifically, the scheme needs modification to address (1) the influence of legal and regulatory
considerations on the initial stages of ecological risk assessment and (2) the importance of characterizing
ecological risks in terms that are intelligible to risk managers. The committee's opinion is that these
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augmentations are as
assessment.
important for human health risk assessment as they are for ecological risk
The Integrated Framework
CRAM concluded that integration of ecological risks into the 1983 risk assessment framework is
preferable to developing a de nova ecological risk assessment framework. Like health risk assessment,
ecological risk assessment must be defined in broad terms if it is to be applicable to the full array of
environmental problems that regulatory and resource management agencies must address. Moreover, any -
framework chosen for ecological risk assessment must be simple, flexible; and general, so that it will be
understood by both scientists and the risk managers with whom scientists must communicate. The 1983
framework, by any measure, has been extraordinarily successful in communicating the broad features of
health risk assessment throughout the scientific and regualtory communities. Although ecological risk
assessment and human health risk assessment differ substantially in terms of scintific disciplines and
technical problems, CRAM concluded that the underlying decision process is the same for both. The
function of risk assessment is to link science to decisionmaking, and that basic function is essentially the
same whether it is risks to man or risks to the environment that are being considered.
The 1983 report defined ^^ identification as "...the process of determining whether exposure
to an agent can cause an increase in the incidence of a health condition," including "...characterizing the
nature and strength of the evidence of causation." Doy-re.sron.se assessrneqt was defined as "...the
process of characterizing the relation between the dose of an agent administered or received and the
incidence of an adverse health effect as a function of human exposure to the agent," accounting for
exposure intensity, age, sex, lifestyle,and other variables affecting human health resposes to hazardous
agents. *vr-— assessment was defined as "...the process of measuring or estimating the intensity,
frequency, and duration of human exposures to an agent currently present in the environment or of
estimating hypothetical exposures that might arise from the release of new chemicals into the
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environment" Risk characteriTatinq was defined as "...the process of estimating the incidence of a health
effect under the various conditions of human exposure described in exposure assessment It is performed
by combining the exposure and dose-response assessments. The summary of effects of the uncertainties
in the preceding steps are described in this step."
On evaluating the consistency of the six case studies presented at the workshop with the 1983
framework, CRAM concluded that most of the case studies fit reasonably well. The most obvious
common deficiency related to risk characterization. Only one of the case studies, the Georges Bank study,
included any quantification of risks in terms that could be used for risk-benefit calculations, valuation
studies, or other quantitative comparisons applicable to decision-making. Even in this case, the value of
the assessment to decisionmaking is uncertain. During plenary discussion, the study author emphasized
that communication between scientists and managers is still inadequate and that fisheries management
actions are often only marginally influenced by quantitative assessments. Approaches to hazard
identification exemplified in the case studies were, on the other hand, substantially more diverse and in
some cases more sophisticated than envisioned in the 1983 framework. Ecological hazard identifications
often include identifications of specific species or ecosystems of interest, delineation of study areas, and
determination of types of laboratory or field data on which an assessment will be based. These decisions
reflect both scientific considerations (which systems are vulnerable? what kinds of effects are possible?)
and management considerations (which species or ecosystems are to be protected? must costs be weighed
against benefits? is the objective to protect the resource or to optimize exploitation of the resource?). The
workshop consensus was that definitions of hazard identification and risk characterization proposed in the
1983 report are inadequate for the purposes of ecological risk assessment
CRAM agreed with the consensus at the workshop that the 1983 framework is inadequate, as
written for application to ecological problems, because (1) it does not account for legal mandates and other
policy, considerations that influence the initial stages and focus of ecological risk assessments, and (2) it
pays insufficient attention to the critical problem of effective commun
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public. The opinion of the committee, however, is that these deficiencies are not unique to ecological risk
assessment. Differences in the functions of different regulatory agencies clearly influence the types of data
and inference guidelines used in health risk assessments, and effective risk communication is as important
(and often as inadequately performed) in health as in ecological risk assessment.
Ha.^ identification was redefined by CRAM to be the determination of whether a particular
hazardous agent is associated with health or ecological effects of sufficient importance to warrant further
scientific study or immediate management action. Fvpnsnr^sponse wwv* was defined as the
determination of the relation between the magnitude of exposure and the probability of occurrence of the
effects in question. Replacement of the term "dose" with a more general term is required, because "dose-
has a distinctly medical connotation and cannot be effectively applied to nonchemical stresses, such as
habitat change or harvesting. The "responses" addressed in ecological risk assessments include both
direct effects of exposure and the much broader indirect effects, such as secondary poisoning of raptors
due to accumulation of pesticide residues in their prey and effects of harvesting on fish-community
structure. Exp^m^SSSsmsiit was defined byCRAM as the determination of the extentof exposure to
the hazardous agent in question before or after application of regulatory controls. In the committee's view,
the term "exposure" can legitimately be applied to nonchemical stresses, including both physical stresses
(such as habitat change and UV radiation) and biological stresses (such as species introductions).
Alternative terms, e.g., "stress" or "stressor" were deemed unsuitable because of conflicts with medical
uses of the same or similar terms. Risk characterisation. was defined as the description of the nature and
often the magnitude of risk, including attendant uncertainty, expressed in terms that are
comprehensible to decisionmakers and the public.
The revised framework is summarized in Figure 1. In addition to the four basic components,
Figure 1 depicts two key aspects of risk assessment As noted above, it is essential to recognize the
influence of policy considerations on hazard identification. CRAM also wanted to emphasize the need to
create a connection between the results of today's risk assessments and the science base for future risk
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assessments. The risk assessment process should not end when a regulatory decision is made. Followup
in the form of monitoring (where measurable effects have been predicted), validation studies, and basic
research are needed to improve the data and models available to technical risk assessors whenever the same
or a similar problem is encountered in the future.
Comparison to EPA's Framework for Ecological Risk Assessment
EPA's recently-published "Framework for Ecological Risk Assessment" [3, 4] is quite similar to
CRAM's integrated framework, and the similarity is not accidental. EPA consciously modeled its
framework on the 1983 NRC health risk assessment framework. Moreover, several of the authors of
EPA's framework document participated in the CRAM ecological risk assessment workshop and a CRAM
member served on a review panel that evaluated EPA's framework [8]. CRAM's "hazard identification"
is replaced by problem formulation in EPA's version. CRAM's "exposure assessment" and "exposure-
response assessment" are subsumed by EPA in a step called Analysis, which is in turn subdivided into
Characterization of Exposure and Characterization of Effects Definitions of the components are more
specifically ecological and somewhat more explicit than are the definitions in the CRAM framework.
Problem formulation, for example, is described as a "systematic planning step" that includes discussions
with risk managers, preliminary description of the potential ecological effects of the stressor, identification
of the specific effects (termed " assessment'endpoints") to be addressed in the assessment, and
development of a conceptual model to guide the assessment
The relationship between assessment and management in the EPA framework reflects EPA's
specifically regulatory mission and might be approached differently by another agency or a private-sector
organization involved in ecological risk assessment Policy input is provided by a risk manager who
discusses the assessment with the technical staff during the problem formulation phase. When the
assessment is complete, the results are discussed with the risk manager, who then is responsible for
making a decision and communicating the results to the public at large. In more general kinds of
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assessments, such as environmental impact assessments performed to satisfy the National Environmental
Policy Act, the planning phase (termed "scoping" in NEPA regulations) includes substantial public
involvement. In others, such as assessments performed during development of environmentally safe
products, frequent iterative interactions between design engineers, marketing staff, and risk assessors
might be expected.
The future of ecological risk assessment
Neither the CRAM framework nor the EPA framework were intended to provide an explicit recipe for the
scientific content of ecological risk assessment. The EPA expects the process of technical guidance
development to implement its framework to take several years [4]. The CRAM report recommends that
expert committees be convened to discuss the major scientific issues in ecological risk assessment. The
report identifies four major areas in which scientific consensus is lacking: extrapolation across scales of
time, space, and ecological organization; quantification of uncertainty; validation of predictive tools; and
economic valuation of ecological resources. The principal objective of both frameworks is to provide a
common conceptual foundation that can enhance the consistency and credibility of ecological risk
assessments.
CRAM made five specific recommendations concerning the future development and use of
ecological risk assessment.:
•Risk assessors, risk managers, and regulatory agencies should adopt a uniform
framework for ecological risk assessment The extension of the 1983 NRC human
health risk assessment framework described in the CRAM report and depicted in
. Figure 1 is general enough to apply to most assessment problems and emphasizes the
common elements of health risk and ecological risk assessment.
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•State and federal agencies should expand the issue of risk assessment in strategic planning
and priority-setting as a means of focusing their resources on critical environmental
problems and uncertainties.
•Agencies should support the development of improved methods of risk characterization
and consistent, guidelines for applying them. Specific areas where current approaches
are inadequate include extrapolation of population and ecosystem effects, expression
of risks in terms that are useful for decision-making and understood by the public at
large, and evaluation and communication of both quantitative and qualitative
uncertainties.
•To improve the science base for future risk assessments, agencies should institute
systematic followup of risk assessments with research and monitoring to determine the
accuracy of predictions and resolve remaining uncertainties.
•EPA and other agencies should support systematic research programs to improve the
credibility and utility of ecological risk assessments, and should draw on scientific
expertise available outside the agencies themselves to develop technical guidance on
the scientific content of ecological risk assessments. „
The intent of CRAM's recommendations is to facilitate understanding of ecological assessment
principles by nontechnical decisionmakers and the public at large and to ensure consistent improvement in
the science supporting ecological risk assessment. The past few years have seen a major increase in public
interest in the environment The adoption of "sustainable development" as general environmental goal
implies that economic development strategies should strive to simultaneously maximize both human
welfare and environmental quality. An integrated framework for risk assessment of the kind
recommended by CRAM can facilitate achievement of this goal.
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Acknowledgements
The author gratefully acknowledges the contributions of (1) all members of the National Research Council
Committee on Risk Assessment Methodology, particularly Chairman Bernard Goldstein and Ecological
Risk Assessment Topic Group members Alan Maki and Warner North, and (2) the staff of the Board on
Environmental Studies and Toxicology, particularly Linda Leonard, James Reisa, Richard Thomas, Mary
Paxton, Kathleen Stratton, and Gail Charnley. Oak Ridge National Laboratory is managed by Martin
Marietta Energy Systems, Inc., under contract DE-AC05-84OR 21400 with the U.S. Department of
Energy.
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References
National Research Council (NRC). Risk Assessment in the Federal Government: Managing the Process.
, National Academy Press, Washington, D.C. (1983)
National Research Council (NRC). Issues in Risk Assessment. National Academy Press, Washington,
D.C.(1993)
Environmental Protection Agency (EPA), Framework for ecological risk assessment. Rep. No.
EPA/630/R-02/001, U.S. Environmental Protection Agency, Washington, D.C. (1992).
Norton, S.B., D. J. Rodier, J.H. Gentile, W.H. van der Schalie, W.P. Wood, and M.W. Slimak. A
framework for ecological risk assessment at the EPA. Environmental Toxicology and Chemistry
11:1663-1672 (1992).
Fogarty, M. J., A: A. Rosenberg, and M. P. Sissenwine. Fisheries risk assessment: sources of
uncertainty. Environ. Sci. & Technol 26:440-447 (1992).
Huggett, R. J., M. A. Unger, P. F. Seligman, and A. O. Valkirs. The marine biocide tributyltin.
Environ. Sci. & Technol. 26:232-237 (1992).
* , '
Kendall, R. J. Farming with agrochemicals: the response of wildlife. Environ. Sci. & Technol. 26:239-
245 (1992).
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Environmental Protection Agency (EPA). Peer review workshop report on a framework for ecological
risk assessment. Rep. No. EPA/625/3-91/022, U.S. Environmental Protection Agency,
Washington, D.C. (1992)
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BIOGRAPHICAL SKETCH: GLEN J. BARRETT, C.I.H.
Mr. Barrett received his undergraduate degree in Chemistry from Marquette University
in 1968. He received his Master's in Inorganic Chemistry, also from Marquette
University, in 1970.
Mr. Barrett is currently a Certified Industrial Hygienist and Health and Safety Officer
with The Earth Technology Corporation. He has more than 15 years of experience in
all aspects of industrial hygiene services, including health and safety program
development and implementation, preparation of hazardous substance fact sheets for
state hazard communication programs, preparation of human health risk assessments,
health and safety training, control technology assessment, and industrial hygiene
program management. He is responsible for development and implementation of Earth
Technology's health and safety programs and management of risk assessment
projects. He has managed air monitoring for major asbestos abatement projects, and
has presented asbestos worker, supervisor, inspector, and management planner
Asbestos Hazard Emergency Response Act (AHERA) training.
Mr."Barrett has developed health and safety plans for the Environmental Protection
Agency, Region 3 and for the U. S. Air Force Center for Environmental Excellence
under the Air Force Installation Restoration Program, and has managed human health
risk assessments for various locations.
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Hazard
Identification
Exposure:
Response
Assessment
Exposure
Assessment
i
Risk
Characterization
Science
research
validation
monitoring
Figure 1. The CRAM integrated human health/ecological risk assessment framework
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A Computer-Aided Approach to Quantification of Human Intake
for Risk Assessments
Glen J. Barrett, M.S., C.I.H.,
Leonard M. Pried, M.S., and Jennifer A. Smith, M.A.
The Earth Technology Corporation
Alexandria, Virginia
A Computer Approach for Risk Assessments
Glen J. Barrett
The Earth Technology Corporation
1420 King Street, Suite 600
Alexandria, Virginia 22314
023«.rcv
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ABSTRACT
A Risk Assessment was conducted by The Earth Technology Corporation to
.valuate risks to human health resulting from soil contamination found at an
evaluate *«* the use of several dBase programs that
rapid review of a large quantity of data.
A total of 858 soil samples were collected and analyzed for 25 inorganic
r: rrr r r r -;=
Restoration Program Information Management System (IRPIMS) format.
A methodical approach was used to select chemicals of potential concern for
risk calculations. Unusable data were deleted from the IRPIMS databases «th the-
aid of several dBase programs. A dBase program, SITESTAT.PRG, was used to
calculate statistical data. This program was executed separately on «*-dual
site databases for all depths sampled and for specific depths -of -concern Data
generated by SITESTAT.PRG included maximum concentration -detected ar.thmet.c
fa:! ^JL**-. *~ »-« of valid detections and the_ total number of
samples analyzed. Analytical data were evaluated wxth a variety of criteria
including frequency and magnitude of detection and consideration of the depth of
concern. A Sase program, ABOVEMAX.PRG, was developed to greatly ^^
comparisons of site inorganic data to background site data. The col ect ve
result of these analyses is a table in which a summary is provided to justify
retlntion or deletion of a chemical of concern for further evaluation for each
site .
Human receptors who could be impacted by migration of site contaminants or
by direct contact with site soil were identified. Soil contact exposure .pathways
were identified for these receptors. Two dBase programs, AIREXPS.PRG and
SOIBEXP.PRG, were .developed by The Earth Technology Corporation to estimate
average exposure and reasonable maximum exposure for identified axr and so.l
exposure pathways.
KEY WORDS: Risk, Intake, Exposure, dBase
023d rev
-------
INTRODUCTION
A baseline risk assessment was conducted as part of a remedial soil
investigation to evaluate baseline risks to human health resulting from soil
contamination found at an industrial facility, A total of 858 soil samples were
collected from 10 potential hazardous waste sites and analyzed for 25 inorganic
and 159 organic chemicals. A risk assessment, was not conducted for groundwater
•contamination because:
• The regional aquifer which supplies drinking water for offsite
residents has been identified as contaminated, and an operating
remediation system is in effect
• The remediation system has impeded migration of contaminated
groundwater off the industrial facility property
• Current and future offsite residents will not be impacted by the
contaminated plume contained on the facility property.
• Uncontaminated drinking water is supplied to facility workers by a
municipal water system.
Several references provided guidance for the baseline risk assessment.
These references included:
• Risk Assessment Guidance for Superfund, Volume I: Human Health
Evaluation Manual (Part A), Interim Final, U.S. Environmental
Protection Agency (USEPA), Office of Emergency and Remedial
Response, Washington, D.C., December 1989
• Risk Assessment Guidance for Superfund, Volume I: Human Health
Evaluation Manual, Supplemental Guidance, "Standard Superfund
Default Exposure Factors", Interim Final, USEPA, Office of Emergency
and Remedial Response, Washington, D.C., March 1991.
Soil analytical data derived from the sampling effort were available in a
set of related databases in the Installation Restoration .Program Information
Management System (IRPIMS) format. These databases were constructed as .DBF
files and were managed using dBase III Plus version 1.0. DBase was chosen for
its applicability and cost effectiveness.
This paper describes the use of several dBase programs to calculate
-------
statistics used for the selection of chemicals of potential concern, to calculate
the average and 95* upper confidence limit (UCL) concentrations for chemicals of
potential concern at identified human receptors, and to estimate human daily
intake values for identified pathways, Stacistical results from these programs
greatly facilitated rapid review of a large quantity of data.
Although a full, baseline risk assessment was conducted, only the following
risk assessment procedures will be discussed:
• Selection of Chemicals of Potential Concern
• Identification of Human Receptors
• Identification of Exposure Pathways
• Estimation of Concentrations of chemicals of potential concern at
human receptors
• Estimation of human intake values.
OVERVIEW OF DATABASE MANAGEMENT
The IRPIMS format required the use of several databases; BCHRES.DBF (batch
result database) was the main database used. It contained the analytical soil
results for each analysis performed on all samples. Specifically, each BCHRES.DBF
record contained the analytical results for a single chemical by sample; a trip,
equipment, ambient air, or laboratory blank; or for a surrogate spike.
BCHRES.DBF was used to construct an individual site database for each site
evaluated. BCHRES.DBF is composed of many fields; only a subset of these fields
were used for the risk assessment. Table I displays the BCHRES.DBF fields used
for the risk assessment.
SELECTION OF CHEMICALS OF POTENTIAL CONCERN
The following criteria were used to initially screen organic and inorganic
soil data:
1 Data Validation. Ten percent of the data were validated in
accordance with USEPA guidelines (1) (2) . All qualified data were reviewed. Table
II presents validation qualifiers used for soil data.
A dBase program inserted 'R' or 'DR' qualifiers in the RAQUALIFY field in
BCHRES.DBF."Any records qualified with a 'R' or 'UR' were considered unusable and
02JS.tr*
-------
Table X .
BCHRES.DBF Fields Used for the Risk Assessment
LOCXREF - A unique identifier for the location of the sample;
typically, the borehole ID
ANMCODE - The analytical method code
EXMCODE - The extraction method code
SBD - The sample beginning depth
SED - The sample ending depth
PARLABEL - Chemical label
PARVQ - Indicator of sample detection
PARVALDLUN - Dry weight soil concentration (mg/kg)
LABDL - Laboratory detection limit
SACODE - Type of sample (blank, field replicate)
SITEID - Number designating the site from which the sample
was obtained
RAQUALIFY - Validation qualifier (U,UJ,J,UR,R)
HTFLAG -. Indicates the sample exceeded a holding time
NDBLANKC - Indicates the sample did not exceed 10 or 5 times,
depending on chemical, the maximum detection of any
blank associated with the sample batch
023«.iCT
-------
Table II
Soil Data Validation Qualifiers
-J IThe associated numerical value is an estimated quantity
R - The data are unusable (compound may or may not be present) ;
Resampling and reanalysis is necessary for verification
CTJ- - The material was analyzed, but the analyte was not detected;
The sample quantitation limit is an estimated quantity
UR - The material was analyzed, but the analyte was not detected;
The data are unusable and is rejected
tr - The material was analyzed, but the analyte was not detected;
The associated value is the sample quantitation limit
OZS&tcv
-------
were, not added to any individual site database. A data qualified with 'J'
or 'UJ' were considered valid and were retained in site databases.
'U'
2. Quality Control Analysis, A dBase program was developed to
evaluate the analytical results for all quality control blanks in accordance with
USEPA Contract Laboratory Program (CLP) guidelines. These blanks included trip
blanks, .equipment blanks, ambient condition blanks, laboratory calibration
blanks, and laboratory method blanks. The dBase program performed two tasks in
the evaluation of quality control blanks:
A. Quality control blanks were analyzed for the presence of common
laboratory contaminants, including acetone, 2-butanone, toluene,
methylene chloride, and phthalate esters. Contaminant detections
• were considered valid only if the analyte concentration exceeded
10 times the maximum analyte concentration in any •blanks
associated with the sample batch. If an analyte concentration did
not exceed 10 times the maximum concentration for an analyte found
in any blanks, the dBase program inserted a 'B' in the NDBLANKC
field of the BCHRES.DBF analyte record. If a 'B' was assigned to
the NDBLANKC field, the analyte concentration was not considered
valid and was deleted from further consideration.
B. For any other contaminant, an analyte detection was considered
valid only if the analyte concentration exceeded five times the
maximum analyte concentration detected in any blanks associated
with the sample batch. If an analyte concentration did not exceed
five times the maximum analyte concentration in any blanks
associated with the sample batch, the dBase program inserted a 'B'
in the NDBLANKC field of the BCHRES.DBF analyte record. If a 'B'
was assigned to the NDBLANKC field, the sample concentration was
not considered valid and was deleted from further consideration.
3. Laboratory Holding Time Analysis. Laboratory holding times are
time constraints for stages in laboratory sample analysis. Laboratory holding
times were evaluated to identify all samples which exceeded any laboratory
holding time with respect to extraction and analysis. Analytical results based
on missed holding times were rejected from further consideration.
To accomplish this task, a dBase program was developed to evaluate missed
holding times by analyzing one of the IRPIMS-formatted databases, BCHTEST.DBF
(batch test database) . In BCHTEST.DBF, each record contained a sample collection
date, an extraction date, and an analysis date. The dBase program evaluated each
-------
record in BCHTEST.DBF to determine if the analysis for a sample exceeded a
laboratory holding time. Each BCHTEST.DBF record was associated wxth a set of
records in BCHRES.DBF which contained the analytical results for the set of
chemicals run for the analysis. For each record in BCHRES.DBF contaxnxng
anllytical results for an analysis which exceeded a holding time, the dBase
^ogram inserted the qualifier 'OUT' in the record's HTFLAG field, Durxng the
construction of individual site databases, any records qualified with 'OUT' ,n
the HTFLAG field were considered unusable.
A dBase program, SITESTAT.PRG, was used to calculate statistics required
for the selection of organics and inorganics of potential concern for each .it..
This program was run separately on each individual site database for the entxre
depth sampled and for a specific soil sample depth-of-concern. - A ^th'^
concern is defined as the soil interval which a receptor could contact. If two
receptors could contact site soil at different depths (e.g., a current worker
contacting surface soil and a future excavation worker contacting subsurface
soil), the greatest depth-of-concern was used to generated statistical datau
Statistics included: the maximum and minimum concentratxon, the average
concentration, the 95% UCL of the arithmetic mean of the analyte s
concentrations, the number of samples evaluated, and the number of Action, for
an analyte. ^ Report Writer was used to print the calculated statxstxcs from
Se statistics databases produced by SITESTAT.PRG and an additional dBase
program, ABOVEMAX.PRG, in table form for each site.
The criteria for retaining or deleting inorganic constituents involved
comparing inorganic concentrations at each site to inorganic concentrations at
"ted background site. To accomplish this, output from SITESTAT.PRG was
evaluated using the following approach. First, SITESTAT.PRG was executed for
each site database for the entire depth sampled. An inorganxc chemxcal whxch was
not detected in any site samples was deleted from further consideration for the
site Second, comparisons were made using the maximum concentratxon and
arithmetic mean for each inorganic chemical at the background site and at each
individual site for all collected samples. Table III provides an example of
statistics calculated by SITESTAT.PRG for inorganics (i.e., arsenxc and
magnesium) for a site. Third, the frequency of detections above maximum
background concentrations for each inorganic chemical within a sample depth-of^
concern was evaluated. This frequency was obtained by usxng output from
SITESTAT.PRG and ABOVEMAX.PRO. Specifically, SITESTAT.PRG identxfxed the number
of samples analyzed for each inorganic chemical within a sample depth-of-concern.
ABOVEMAX.PRG identified the detections above maximum background concentratxons
O&tf.iw
-------
: •.•.',;':•„;,: ...',;. . . sable iix
; Statistics Data
PARLABEL
MAX
MAX
SITEID
MAX
UNITS
MAX
SBD
MAX
SED
MAX
QUAL
AR
MEAN
ARMUCL
95
SP
SIZE
DETECTS
Inorganics
Arsenic
Magnesium
14.4
21765
1
1
mg/kg
mg/kg
10.0
10.5
10.5
11.0
5.1
8083
5.2
8623
173
158
2
158
Organics
Bis (2 -ethyl -
hexypthalate)
Xylenes
5.5
0.108
1
1
mg/kg
mg/kg
11.0
10.5
11.5
11.0
0.7
0.003
0.8
0.004
108
191
37
5
Key:
PARLABEL
MAX
MAXSITEID
MAXUNITS
MAXSBD & MAXSED
MAXQUAL
ARMEAN
ARMCJCL95
SPSIZE
DETECTS
Analyte label
Maximum concentration
Number designating the site from which the maximum sample was
obtained.
Units of MAX
Max's sample beginning depth and ending depth, respectively
Qualifier for the maximum concentration; if the MAX value was a
nondetect, an 'ND' would appear in this field
Arithmetic mean
95% UCL of the ARMEAN
Number of samples analyzed
Number of valid detections
Note: This table was created from a SITESTAT.PRG database.
-------
for each inorganic chemical for all depths. The frequency of detections above
maximum background concentrations for each inorganic chemical was manually
calculated by dividing the number of detections above maximum background
concentrations within the depth-of-concern by the total number of samples wichin
the depth-of-concern. Fourth, the magnitude of detections above maximum
background concentrations within the depth-of-concern was evaluated using output
from ABOVEMAX.PRG. Table IV provides an example of magnesium detections above
maximum background concentrations for a site.
For selection of organic chemicals of potential concern, a similar
evaluation process was utilized with the aid of SITESTAT.PRG output. For organic
chemicals, however, the background site was not used as a. comparison.
Consequently, ABOVEMAX.PRG was not used. The primary analysis was made using
frequency and magnitude of detections. First, as previously described,
SITESTAT.PRG was executed for the entire depth sampled. All organic compounds
which were detected in less than 5 percent of the site samples were eliminated
from further consideration. Second, SITESTAT.PRG was executed for each site
database for the depth-of-concern. The frequency of detections within the depth-
of-concern was evaluated. Third, dBase was used to construct a database of
detections for all depths. The magnitude of each detection within the site
depth-of-concern was evaluated. In general, compounds which were not detected
at levels significantly greater than the laboratory detection limit were
eliminated from further consideration for the site. Tables 3 and 4 provide
examples of SITESTAT.PRG results used to justify retention or deletion of
organics of potential concern.
Table V provides an example of justification for retention or deletion of
inorganic and organic chemicals for a site, using criteria previously discussed.
Figures 1 and 2 provide a general description of the steps performed by
SITESTAT.PRG. It is beyond the scope of this paper to provide a detailed
10
-------
Table IV
Detection By Depth
Inorganics1 :
(only concentrations above the background level are given)
Site
ID
1
1
1
1
PAR
LABEL
Mg
. « Mg
Mg
Mg
LOCXREF
MWA1
MWA1
MWA1
MWA1
ANMCODE
SW6010
SW6010
SW6010
SW6010
SBD
10.5
90.5
120.5
215.5
SED
11.0
91.0
121.0
216.0
PARVQ
=
=
s
=
LABDL
50
50
50
50
PARVAL-
DLOM
21,765
16,230
16,216
18,003
Organics2
Site
ID
1
1
1
1
1
PAR
LABEL
Xylenes
Xylenes
Xylenes
Xylenes
Xylenes
LOCXREP
MWA1
MWA1
MWA1
MWA1
MWA1 .
ANMCODE
SW8240
SW8240
SW8240
SW8240
SW8240
SBD
10.5
70.0
80.5
90.0
100.0
SED
11.0
70.5
81.0
90.5
100.5
PARVQ
=
=
=
=
=
LABDL
0.005
0.005
0.005
0.005
0.005
PARVAL-
DLUM
0.108
0.018
0.013
0.027
0.017
Mg
Xylenes
Magnesium
Total Xylenes
'inorganic'data table was created from an ABOVEMAX.PRG database.
Organic data table was created using dBase.
11
-------
Inorganics
Silver
(Ag)
Arsenic
(AS)
Magnesium
(Mg)
Mercury
(Hg)
Zinc
(Zn)
Bis (2-
ethylhexyl)
phthalate
(BEHP)
Chlorobenzene
Total Xylenes
. . Table V
Examples of Justification for Selectionc-i
Chemicals ot Potential Conee«i at a Site
Explanation
Ag is detected in only l of 131 sample
mean.
f
for the site is less than the background
concentrations are considered to be at background
Only 4 of 158 sample concentrations exceed the
background level. Three of the samples were taken from so:.!
moan nnlv sllcrntJ-V exceeue unc uo.v-r>.aj.——-—
C^entrations9arey considered to be within background at the
soil depth-of-concern.
sample r-nnrAntrations are below
limit.
All site
detection
background levels.
of
concentrations are
Concentrations ar
the laboratory
considered to be at
. - - - - — : - --
Five of 158 sample concentrations exceed the maximum
background level ^Pour of these samples were taken from soil
ensure concern (146 mg /kg wfll 375 mg/kg at 0 0
BEHwas detected in 37 o, : 10 e » sa^ e^
signriic^tly" "exceed t'he laboratory detection limit. Because
of the sionificant concentrations in the soil deptn-ot
concern, alid because of the relatively high ^^/^ection
frequency (34%), this analyte is- retained for turther
cons ideration .
CUHtjJnacJ.&i—IWA* . _^ . —,*«/•77
sjsnssr-r. trr??Jf, •«-. SUSE^-S^ s
5% Three of the detections are very low, only
exceeding the detection linit. Consequently, the
detection frequency is 4*. Chlorobenzene is deleted from
further consideration. _ .
Total xylenes were detected in 5 of 191 samples (0.013 to
0 0108 mg/kg at 10.5 to 100.0 feet). Because of low sample
detection^ frequency (3*) and because there is only one
detlctio^ JlttSTtS soil depth:of-concern, total xylenes is
deleted from further consideration. ,
Status
Deleted
Deleted
Deleted
Deleted
Retained
Retained
Deleted
Deleted
OZ&n*
12
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( BEGIN J
Set the Soil Interval
Depth erf Concern
'MAIN LOOP?
Process Site
' Database RecordsN
v Sequentially Until y
the End of
Database
Initialize Statistical Variables.
Identify Current Chemical to
be Processed.
Delete all records from the working
database UCLFILE.DBF. Chemical
sample concentrations are placed in
UCLFILE.DBF to calculate the 95% UCL
of arithmetic mean of the
concentrations.
INNER LOOP: \
Process Each \
Record Sequentially \
for the Current J
Chemical Being /
Processed /
Concentration
Subroutine
I Skip to Next Record |
j End Inner Loop j
Statistics.
Subrouting
\ End Main Loop |
' 7h* E*rth Tvchnolcgy
Corporation
FIGURE 1
SITESTAT.PRG FLOW DIAGRAM
•0236.FK3
13
-------
( BEGIN
Concentration*
I Subroutine
3
Sample Concentration -
Laboratory Detected Concentration.
Increment Number of Detections
by One.
Sample Concentration -
Lab Detection Limit -0.5
Add Sample
Concentration to Sum for
Arithmetic Moan
Increment Number of
Samples Processed by
One
Append Sample
Concentration to
Working Database
UCLFILE.DBF
Hon GraaUr
thtn *• Working
Uudmwn U»InUJmd (or
ihtCtwmlcJl?
Conanlralfon l**» timn
«w Working Minimum
Reset Working Maximum to the
Sample Concentration. Reset
Working Sample Parameters
for the Maximum including:
LOCXREF. SACODE,
ANMCODE. EXMCODE, SBD,
SED, RAQUALIFY
Reset Working Minimum to the
Sample Concentration. Reset
Working Sample Parameters for
the Minimum including:
LOCXREF. SACODE.
ANMCODE. EXMCODE. SBO,
SED. RAQUAUFY
BEGIN
Statistic*
Subroutine
Calculate Arithmetic Mean -
Sum of Sample Concentrations /
Number of Samples
Open Working Database
UCLFILE.DBF. Use the Recorded
Sample Concentrations to
Calculate the Standard Deviation
for the Concentrations.
Obtain t-value from Database
TVALUE.DBF. Calculate 95%
UCL of the Arithmetic Mean of the
sample concentrations.
Record Statistics for the Chemical in
Database SITESTAT.DBF. Record
the soil depth-of-concem. Record
the Maximum and Minimum
Chemical Concentration and the
Associated Parameters. Record the
Number of Samples, Number of
Detects. Arithmetic Mean. 95% UCL
of Arithmetic Mean, and the
Standard Deviation for the Sample
Concentrations.
f END ^
Statistic* I
I Subroutine J
END
Concentration
Subroutine
D
1 ThiEfrth Technology
Corporation
FIGURE 2
SITESTAT.PRG FLOW DIAGRAM
SUBROUTINES
KOOS.FK3
-------
discussion of SITESTAT.PRG. Note that the program requires creation of an index
file for analysis of a site database. The index file must group all samples for
an analyte in ascending order according to the sample beginning depth. This is
accomplished by indexing the site database using the concatenated fields
PARLABEL+SBD.
IDENTIFICATION OF HUMAN RECEPTORS
Conceptual site models were developed for each site. Each conceptual site
model includes a description of previous or current activities, including
chemical sources and amounts', identification of chemicals of potential concern
which can be attributed to site activities, identification of migration pathways,
and identification of current or future human receptors who could be impacted by
contact with contaminated site soil or chemicals migrating from a site.
Three migration pathways were identified whereby chemicals of potential
concern could potentially migrate toward human receptors. These migration
pathways are:
• Migration of volatile .organic compounds (VOCs) from the soil
saturated and unsaturated zone -to air
• Migration of dust containing particulate-bound contaminants to air
• Transport of surface soil contaminants by ephemeral surface water
runoff.
Human receptors who could be impacted by migration of site contaminants or
by direct contact with site soil were identified. These receptors are: current
workers, current offsite resident children, and future excavation workers.
15
-------
First, current workers were identified who could contact contaminated site
soil directly. These receptors were workers in buildings located on sites with
contaminated soil or workers whose activities brought them in contact with site
soil, such as workers who jog across a site with contaminated soil. Second,
onsite current workers were identified who could be impacted by migration of VOCs
from the site soil saturated and unsaturated zone or migration of contaminated
dust to air. Third, offsite current workers were identified who would be
maximally impacted by VOCs migrating off site from soil or by dust containing
particulate-bound contaminants migrating off site. Fourth, current resident
children (i.e., 6 years of age) were identified as a sensitive subpopulation who
could be impacted by contaminants migrating from a site in ephemeral surface
water runoff through drainage channels and ditches. Finally, future receptors
were identified who could be impacted by onsite excavation activities.
investigations revealed that excavation activities could occur in the future at
three sites. Therefore, future excavation workers were identified at these sites
who could be impacted by airborne VOCs or contaminant-bound dust, or who could
contact the contaminated soil directly.
IDENTIFICATION OF EXPOSURE PATHWAYS
Exposure pathways were identified for human receptors who could be exposed
to a chemical through a migration pathway or by direct contact with the chemical
in soil.
The following exposure pathways were identified for current worker
receptors who could be impacted by soil migration pathways or who could contact
soil directly:
• incidental ingestion of contaminated surface soil
• Dermal absorption of chemicals from surface soil
• Inhalation of contaminated dust
16
-------
• Inhalation of VOCs.
Exposure pathways for offsite resident children who could contact chemicals
in soil transported from a site in the drainage channels and ditches are:
•' Incidental ingestion of contaminated surface soil
» Dermal absorption of chemicals from surface soil.
The existence of surface water in the drainage channels'and ditches is
temporary (i.e., on the order of hours), and only 11 inches per year of
precipitation falls at the facility. Consequently, the exposure of offsite
children to contaminated surface water is negligible, and resultant surface water
exposure pathways are considered to be incomplete.
The following exposure pathways were identified for future excavation
workers:
• Incidental ingestion of contaminated subsurface soil
• Dermal absorption of chemicals from subsurface soil
• Inhalation of contaminated dust
• Inhalation of VOCs.
DETERMINATION OF EXPOSURE CONCENTRATIONS AT RECEPTORS
Both the arithmetic mean and 95% UCL of the arithmetic mean contaminant
concentrations at receptors were quantified per federal and regional USEPA
guidance. Because data were not collected in an unbiased manner and goodness-of-
fit statistical tests rejected log-normal distribution of a subset of soil data,
data were assumed to be normally distributed. Consequently, arithmetic
concentrations, rather J:han geometric concentrations, were quantified at receptor
intake locations.
17
-------
.Table VI presents the formulas used to quantify the arithmetic mean and 95%
UCL concentration at a receptor for each chemical of potential concern:
Soil Contaminants
^ *. _=i ™,iat-P 'site soil mean and 95% UCL
The SITESTAT.PRG program was used to calculate site
exposure concentrations at receptors for varying exposure depths - of - concern. As
stated previously, if a 'B< was assigned to the NDBLANKC field or a. 'OUT' was
assigned to the HTFLAG field, reported analyte concentrations were considered
unusable. Nondetectable concentrations were represented by an •»>' in the PARVQ
field of BCHRES.DBF. In accordance with USEPA guidance, for all concentrations
of chemicals of potential concern which were reported as nondetec.able
concentrations (i.e., the chemical concentration does not exceed the laboratory
detection limit) , a value of one-half the laboratory detection limit was used for
calculating exposure concentrations by SITESTAT.PRG (3) . All valid analyte
detections were represented by '=' in the PARVQ field in BCHRES.DBF. If an '='
was assigned to the PARVQ field, the dry weight soil concentration in the
PARVALDLUN field was used by SITESTAT.PRG to calculate exposure concentrations.
in those cases where the 95% UCL of the mean concentration exceeded the maximum
detected chemical concentration, the maximum detected concentration was used as
che exposure concentration. Examples of arithmetic mean and 95% UCL exposure
concentrations have been presented in Table III.
For current worker soil pathway exposures, sample data collected from the
surface interval were used to calculate surface soil concentrations. For future
excavation worker exposure, sample data collected from assumed subsurface
excavation depths (e,g., 20 feet depths for two sites, 10 feet depth for one
' site) were used to calculate soil exposure concentrations.
For the current resident child exposure, the resident child was assumed to
be exposed directly to chemical concentrations detected in site surface soil.
OZ3«,rev
18
-------
••.. • ••"••'•'•'•'•.. ';<:•'••;':"••-': .••'v-:-y!-;.r.-?V; -• Table VI ''••.. . " : • .-• .
Formulas : Used tb; Calculate the ..Arithmetic Mean, and 95% UCI*
of the Arithmetic Mean Concentrations at Human Receptors
Arithmetic Mean:
where :
x
n
Q
The arithmetic mean concentration
Number of contaminant samples
Contaminant concentration.
95% UCL:
95% UCL of the arithmetic mean = x
„_,)
where: x = The arithmetic mean concentration
fco.95.D-I = T*16 9s* fc distribution value for n-1 'degrees of
freedom
s = Standard deviation
n = Number of contaminant samples.
19
-------
It was conservatively assumed that site chemicals were totally transported to
resident child receptors by surface water runoff, without any chemical dilution
(eg., adsorption to soil). Consequently, the site surface soil chenucal
j *. v,o t-he surface soil exposure
concentrations calculated are assumed to be the surra<-«
concentrations for the resident child.
Air Contaminants
Soil gas data were used to calculate VOC concentrations in air for current
worker and future excavation worker exposure. The Farmer Model was used to
determine emission rates of VOCs from each site to determine exposure
concentrations for current workers. The Farmer Model is a modified Fick's First
Law for steady-state diffusion. Fick's First Law assumes that transport of a VOC
through the soil cover layer is controlled by molecular dn.ffus.on. It does not
account for the effects of atmospheric conditions, such as temperature, wxnd
speed, and barometric pressure, upon emission rates.
Using chemical air diffusion coefficients for each VOC and assuming the
total soil porosity and air-filled soil porosity of the site-specific .oil to be
30% and 10%, respectively, the Farmer Model was applied to the average soil gas
concentration and 95% UCL of each compound. The VOC emission rates were
determined in units of milligrams per square meter-second (mg/m*-s) . The total
mass of VOCs emitted per second was determined by multiplying the VOC emission
rate by the area (in square meters) of each site.
To determine the ambient air concentration of VOCs for onsite or offsite
worker receptors, the Industrial Source Complex-Long Term (ISC-LT) dispersion
model program was used. The ISC-LT is a USEPA-approved Gaussian dispersion
model. The model operates in both long-term and short-term modes. The model
uses meteorological data, including wind speed, wind direction, and atmospheric
stability class, and area or point-source chemical airborne concentrations, to
determine impact at a receptor location. The model program was used in the area
source and long-term modes to estimate worst-case airborne VOC concentrations at
maximally impacted worker receptors. The location of the nearest (onsite or
offsite) suitable receptor was used to determine the maximum exposure for that
site.
The ISC-LT dispersion model was used to estimate exposure concentrations
for offsite, maximally exposed workers. These workers were identified by
selecting the building at which the model predicted a maximum concentration. The
ISC-LT dispersion model was also used for onsite worker receptors because it
predicts long-term exposure concentrations needed to quantify chronic worker
023&IW
20
-------
exposure.
The average and 95% UCL onsite or offsite worker exposure concentrations,
in milligrams of VOC per cubic meter of air, were determined for each detected
voc.
For future exposure, an assumption was made that all VOCs in the soil would
be released to the atmospheric excavation volume. The VOC concentration in the
air per hour can be calculated using the box model method. The box model method
assumes steady-state, hourly emission rates and uniform dispersion conditions so
that VOC emissions are uniformly distributed throughout a "box" which is defined
by the area of the source and the mixing height. The box model is.applicable for
estimating airborne VOC exposure concentrations for excavation workers because
it uses short-term conditions (e.g., average annual windspeed, hourly emission
rates) to estimate exposure concentrations for short-term excavation tasks.
Applying the box model method to the average and 95% UCL concentration of
VOCs in the soil gas yielded the average and 95% UCL air concentration of VOCs
which may be inhaled by excavation workers.
Inhalation of contaminated dust by current workers was also investigated.
Particulate matter (PM-10) data for the site vicinity were obtained, including
the 24-hour maximum concentrations and the quarterly averages for 1990 and 1991.
The average and 95% UCL concentrations for chemicals of potential concern
were determined from surface soil sampling results. Chemical concentrations in
airborne dust were calculated by multiplying the chemical surface soil
concentration by the appropriate PM-10 air concentration. The average PM-10
value and an average surface soil concentration were used to determine an average
dust concentration. The maximum PM-10 value and the 95% UCL surface soil
concentration were used to determine 95% UCL dust concentrations.
For future exposure pathways, dust concentrations inhaled by excavati'on
workers were determined using similar parameters and assumptions used in the
determination of VOC exposure concentrations. Using assumed excavation
parameters, a particulate emission rate for backhoe excavation work was obtained
from the USEPA Air/Superfund National Technical Guidance Study Series, Volume
III, January, 1989 (5).
OZM.rev
21
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•„„ t-vio box model method, the total
Mt.r determine . mixing volume u.=.ng the ^» «o l,te
»,ount (in ..trie tons, of soil to be removed .as multWl" W
in air
to the calculated concentrations of
concentrations of chemicals which may
ESTIMATION OF DAILY INTAKE VALUES
be inhaled by excavation workers.
intake values were estimated for identified exposure pathways Human
intake (i.e., the magnitude of exposure) is expressed as the amount of dta-xcal
Tan exchange boundary (e.g., skin, lungs, gut) which is ava, , le f r
GDIs were estimated for an exposure of 7 years to a lifetime and SDIs were
estimated for exposure of two weeks to 7 years (3) .
The basic formula used to estimate CDI or SDI is presented in> Table VII.
As previously described, SITESTAT.PRG was used to calculate the mean and
95% UCL of the mean soil chemical concentrations at receptors *«^-£-
These data were established in statistical databases by SITESTAT^
Concentrations of VOCs in air at receptors were estimated using the Farmer Model
and air dispersion modeling. «-lO data and surface soil concentrations were
"ed to determine dust concentrations at receptors. The arithmetic mean chemical
concentration was used to quantify average intake; the 95% UCL of the arithmetic
"emical concentration was used to o^antify the reasonable maximum ensure
accordance with USEPA (3) . As stated previously, if the 95% UCL
exceeded the maximum concentration for a chemical, the maximum
concentration was .used.
Standard default exposure factors were used to estimate intake .where
applicable (3) (4) ; reasonable assumptions were made to quantify site-specific
exposure factors.
The exposure frequency for all permanent current workers and future
excavation workers was assumed to be 250 days/year (4) . The assumption is made
that an adult is at work 5 days/week for 50 weeks/years. Workers were assumed
OZ3«.rev
22
-------
,,.. .....,... . ...
Biasic Formula Used to Bstiifiater Intake. Values
CD/ or SDI (mg/kg-day) = C x
CR x EF x ED
BW
J_
AT
Where:
GDI or SDI = GDI or SDI by the receptor in mg/kg body weight-dry
C = Chemical concentration; the arithmetic mean or 95%
UCL of the mean concentration contacted over the
exposure period
CR = Contact Rate; the amount of contaminated media
contacted per unit time or event
EF = Exposure Frequency (days/year)
ED = Exposure Duration (years)
BW = Body Weight of receptor; the average body weight over
the exposure period (kg)
AT = Averaging Time; period over which the exposure is
a.veraged (days) .
23
-------
to jog on a contaminated site for 30 minutes/day, 5 days/week, and 50 weeks/year.
Resident children were assumed to play off site in the drainage channel for
4 hours/day (exposure time) at 1 day/week for 50 weeks/year, or 50 days/year
(exposure frequency).
The exposure duration for future excavation at two sites was assumed to be
j ,.„*-•! ^n -For future excavation at the
150 hours (i.e., 0.075 year); the exposure duratxon for rutu
third site was assumed to be 500 hours (i.e., 0.25 year).
Two dBase programs, AIREXPS.PRG and SOILEXPS.PRG, were developed to'
estima" average exposure and RME for identified air and soil ensure^athways
respectively. These programs implemented standard default and site specific
exposure factors for each identified exposure pathway. For each site with an
idlntlfiefvoc or dust exposure pathway(s) , AIREXPS.PRG used the arithmetic mean
Ind 95% So, of the mean chemical concentrations for VOCs or dust at site
receptor(s) to quantify the average and RME for each chemical.
For each site with an identified soil exposure pathway(s), SOILEXPS.PRG
used thl arctic mean and 35, UCL of the mean chemical concentration at site
receptor(s) to quantify the average and RME for each chemical.
CONCLUSIONS
DBase was used to manage several IRPMIS-formatted databases containing
analytical soTl data for several hazardous waste sites. Several dBase programs
were developed to automate portions of the risk assessment process, including the
provided several advantages:
. Analytical soil data were evaluated for consistency and completeness.
. A large quantity of data was evaluated within a short timeframe.
. Accuracy of the risk assessment calculations was improved.
. A large quantity of data was evaluated in a cost effective manner.
ACKNOWLEDGEMENTS
Numerous Earth Technology personnel were involved in the risk assessment
process. In particular, the authors would like to acknowledge ?^a««fl«.
Pristine Pryately, Sandra Karcher, Natalie Paul, Pamela Anderson, and Dana Lynn
24
-------
Bowers for their creative and diligent efforts.
REFERENCES
(1) U.S. Environmental Protection Agency, Region I Laboratory Data Validation
-- Functional Guidelines for Evaluating Inorganics Analyses. Washington,
D.C., June, 1988.
(2) U.S. Environmental Protection Agency, Region I Laboratory Data Validation
-- Functional Guidelines for Evaluating Oraanics Analyses. Washington,
D.C.,' February, 1988.
(3) U.S. Environmental Protection Agency, Risk Assessment Guidance for
Superfund, Volume I: Human Health Evaluation Manual (Part A). Interim
Final, Washington, D.C., December, 1989.
(4) U.S. Environmental Protection Agency, Risk Assessment Guidance for
Superfund. Volume I; Human Health Evaluation Manual Supplemental Guidance,
"Standard Default Exposure Factors". Interim . Final, Washington, D.C.,
March, 1989.
(5) U.S. Environmental Protection Agency/ Air/Superfund National Technical
Guidance Study Series, Volume III, Research Triangle Park, NC, January
1989.
25
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BIOGRAPHICAL SKETCH: DR. JAHUSX Z. BYCZK0WSKX
Dr. Byczkowski is currently a Project scientist and Study
Director at ManTech Environmental Technology, Inc. in
Dayton, OH. He received his Master's Degree in Toxicology
from the School of Pharmacy, Academy of Medicine in GdansK,
Poland in 1970. His Ph.D. in Pharmacology and D.Sc. in
Biochemical Pharmacology were received from Academy of
Medicine in Gdansk, Poland in 1975 and 1979, respectively.
He holds a professional license of Pharmacist since 1970.
Prior to joining ManTech in 1991, Dr. Byczkowski held a
tenured position at the Academy of Medicine in Gdansk,
Poland, was a Cancer Research Scientist in Roswell Park
Memorial Institute in Buffalo, NY, was involved in research
and teaching in Toxicology Program at University of South
Florida, college of Public Health in Tampa, FL, and served
as consultant to Pharmaceutical Specialties, Inc. in Tampa,
FL.
Dr. Byczkowski has been the author/co-author of 57
publications in peer-reviewed journals, 3 book chapters,
made 50 presentations at meetings and was invited to make 12
presentations at different Universities. He is a member of
The Society of Toxicology, The Oxygen Society, International
Society for Free Radical Research, Society for Research of
Polyunsaturated Fatty Acids, American Association for the
Advancement of Science, American Museum of Natural History,
The New York Academy of Sciences.
-------
LACTATIONAL TRANSFER OF TETRACEELOROETHYLENE IN RATS
'Jamusz Z. Byczkowski and 2Jeffrey W. Fisher
'ManTech Environmental Technology, Inc., Dayton, OH and
2Toxicology Division, Occupational and Environmental Health
Directorate, Armstrong Lab., WPAFB, OH.
Abbreviated title: LACTATIONAL TRANSFER OF PCE
Send correspondence to: Janusz Z. Byczkowski, ManTech
Environmental Technology, Inc., P.O. Box 31009, Dayton^ OH
45437-0009.
ABSTRACT:
Tetrachloroethylene (PCE) is a commonly used organic solvent and a suspected human
carcinogen, reportedly transferred to human breast milk following inhalation exposure. Transfer
of PCE to milk may represent a threat to the nursing infant. A physiologically-based
pharmacokinetic (PB-Pk) model was developed to quantitatively assess the transfer of inhaled
PCE into breast milk and the consequent exposure of the nursing infant. The model was
validated in lactating rats. The model described the distribution of inhaled PCE in maternal
blood and milk as well as the nursed pup's gastrointestinal tract, blood and tissue. Lactating
Sprague-Dawley rats were exposed to PCE at concentrations ranging from 20 ppm to 1000 ppm,
via the inhalation and then returned to their nursing, 10-11 days old pups. PCE concentrations
in the air, blood, milk and tissue were determined by gas chromatography (GC) and compared
to model predictions. Several prognosis for kinetics of PCE distribution in exhaled air, blood
and milk of exposed human subjects were run and compared with limited available human data
from the literature. It is concluded that the PB-Pk model described fairly accurately the
concentration of PCE in both lactating rats and humans.
KEY WORDS: TETRACHLOROETHYLENE, PERCHLOROETHYLENE, BREAST MILK,
RAT, HUMAN INFANT.
1. INTRODUCTION
Transfer of toxic chemicals via mother's milk represents an important, although not widely
recognized health risk to the infant. An evaluation of the hazards of exposure to occupational
chemicals transferred from mother to baby must include qualitative and quantitative
determinations of chemicals that contaminate breast milk.
A review of the literature conducted by Cone et al. (1) for the USA Environmental
Protection Agency revealed that many chemical compounds may be transferred with breast milk
during feeding. Most of these chemicals were either environmental pollutants or drugs. An
extensive list of the chemicals detected or excreted in human milk (about 150 compounds) has
-------
been published recently by Giroux et al (2). The lactational transfer of both environmental
oollutants and drugs was reviewed in two recent publications (3, 4).
Volatile organic solvents deserve special attention because, these chemicals are widely used
in industrial facilities. Inhaled volatile organic chemicals quickly transfer to systemic circulation
where they selectively partition into fat stores, including breast milk. The residence time for
volatile organic chemicals in the body (including breast milk) is not long when compared to
rahfent environmental contaminants such as polychlorinated biphenyls, but die levels achieved
Ke fat stores such as breast milk may be substantial. For instance, tetrachloroethylene syn.
Perchloroethylene or PCE) was detected in milk from one Canadian woman who regularly
vTsUedTr husband during his lunch hour at a dry-cleaning factory (5). ^™**?«%*
PCE in breast milk was 10 ppm one hour after the visit and over the next 24 ^hours fe » PCE
concentration in breast milk decreased to 3 ppm. Her infant developed Jaundice at Ae age of 6
weeks, but recovered quickly after cessation of breast-feeding (5). While the disease was
attributed to the contamination of breast milk with PCE, it is not clear whether the association
with obstructive jaundice was casual or spurious. ... < •
PCE is a volatile, nonflammable liquid widely used in the dry cleaning industry and in
metal degreasing operations. Acute inhalation of PCE vapor by humans has produced central
nervous system depression ranging from lightheadedness and muscular incoordmation at low
concentrations, to loss of consciousness and respiratory paralysis at higher doses (6-9). The
development of minor, reversible hepatic dysfunction several days following accidental human
exposure to anesthetic concentrations of PCE have been noted also (6,7).
There was limited information in the literature on tissue concentrations of PCE in rats or
mice resulting from test exposures from which a PB-Pk model describing the pharmacokinetics
of PCE was developed and validated (10). But there was no information available on
pharmacokinetics of PCE excretion with milk.
In this report a PB-Pk model for lactational transfer of PCE is described and validated in
nursing rats. Several computer simulations and prognoses for the long-term PCE distribution . in
exhaled air, blood and milk of exposed human subjects were done and compared with available
human data from the literature. The computer simulation of the kinetics of lactational transfer
of PCE may aid a quantitative assessment of the dose passed by the exposed mother to the
nursing infant.
2. MATERIAL AND METHODS
* * The* amount of PCE metabolized by animals in a closed gas uptake chamber (7.9 L) was
measured by gas chromatographic analysis as described by Gargas et aLJJLl). The amount of
PCBtathe sampled air was measured by the HP 5890 Series E GC with FID detector. For each
gas uptake run, three lactating female Sprague-Dawley rats were used.
The decrease in PCE chamber concentration is indicative of the rate of metabolism of Ifte
chemical by the animal (11). Analogous to the description of kinetics for isolated enzymes by
Selis and Menten theory, a "pseudo VMAX" and an "apparent KM" were determined along
with a first order "metabolic rate constant, KFC" by PB-Pk modeling.
2.2. Blood and Milk Analysis . ,
Samples of 80 pL of blood or milk were collected in triplicate from each animal into glass
-------
capillary tubes and then transferred directly to autosampler vials and extracted using n-hexane.
The extracts were analyzed by gas chromatograph equipped with a Vocol™ fused silica column
and an electron capture detector. Calibration curves were prepared and evaluated statistically for
the best fit. Concentrations in blood and milk were corrected for appropriate extraction
efficiency, determined by spiking blood and milk with PCE (96.2%±1.5 and 95.1%.+1.4,
respectively; n=9). The standards were processed also with each series of samples.
2.3. Tissue Analysis
Tissues of pups euthanitized with CO2 and bled, were either placed in sample bags and
frozen in liquid N2 or placed in jars with n-hexane and processed fresh. Thawed or fresh samples
were homogenized and extracted with n-hexane. The extracts were analyzed by GC analogous
to blood and milk extracts. The difference between extraction efficiency calculated for frozen
tissues (56.13%Ji6; n=4) and fresh tissues (56.73%±9; n=3) was insignificant. Calibration
curves were prepared using tissues of control pups spiked with known amounts of PCE.
2.4. Determination of Partition Coefficients
A smear method was used for determination of tissue, milk and blood partition coefficients.
The fresh or frozen tissues were homogenized and about 0.1 g of muscle, liver, kidney, pup,
or 0.05 g of adipose tissue, or 250 pL of blood or milk were smeared on the walls of tared, 25
mL, borosilicate glass scintillation vials. The vials were weighed, sealed and then injected with
known amounts of PCE from an equilibrated standard bag. The vials were then incubated with
vortexing for 3 h at 37°C. Aliquots of 1 mL of head space were injected automatically onto the
gas chromatograph and analyzed as described above for blood and milk. The blood/air and
tissue/air partition coefficients were then calculated, essentially as described by Gargas et al.
(12).
2.5. Animal Exposure
Lactatihg female Sprague-Dawley rats (body weight 232 - 352 g) were used as test animals.
After delivery litters, were reduced to 8 pups per dam and kept undisturbed for 10 to 11 days
(body weight of pups 16.2 - 27.9 g). On day 10 or 11 post portion, the lactating females were
exposed to PCE either in the closed gas uptake chamber (3 rats per 7.9 L chamber) for up to
6 hours or in an open inhalation chamber (5 rats per 30.0 L chamber) for 1 to 6 hours. Numbers
of rats included in the gas uptake and in constant concentration exposures are shown in captions
to figures (typically, 5 groups of 5 dams).
Inhalation was selected as the route of administration most relevant to occupational exposure
of women. Concentrations for inhalation exposures of rats were set between 20 and 1000 ppm
for 1 to 6 hr. At a selected exposure level (600 ppm) the time-dependent, measurements were
made in dam milk and blood after 1, 2, 3, 4, and 5 h exposure to PCE. Another group of dams,
exposed to 600 ppm of PCE for 2 h, was returned to their nursing pups, and blood as well as
tissue levels of PCE were measured in pups at selected times after the maternal PCE exposure.
The animals used in this study were handled in accordance with the principles stated in the
"Guide for the Care and Use of Laboratory Animals" prepared by the Committee on Care and
Use-of Laboratory Animal Resources, National Research Council, Department of Health and
Human Services, National Institutes of Health, Publication No. 86-23, 1985; and the Animal
Welfare Act of 1966, as amended.
-------
in S.MUSOLV, a Fortran-based contocus
^
(VAX8530, Digital Equipment Corp., Maynard, MA).
3. RESULTS
(13). Additional compartments were added to describe milk (14) and ^WM.to*Uy>
for simplicity the pups were described by the lungs, arterial and venous blood, and the other
tissue? ^^compartment (16). However, this simplified model did not describe adequately the
of PCE in pup's blood and tissues. Milk was retained in the gastrointestinal ttac of
pups which apparently delayed absorption of PCE for several hours. To describe this
, ^additional compartment was added to simulate the gastrointestinal tract of pup
(Fig; 1).
equations describing each compartment building the PB-Pk
model for lactational transfer of PCE (schematically shown in Fig. 1).
For well stirred compartments without metabolism or other losses (fat tissue, slowly
perfused and rapidly perfused tissues, pup tissues) the amount change over time is described as
follows:
Concentration in the tissue, Ci, is equal to AIM where Vi represents the volume of the i-th
compartment). ^ & ^ ^ (RAM) is added to the weU
description to account for metabolism (equal to VMAXC*CVL/(KM+CVL)+^*C ^
where VMAXC is pseudo-maximal velocity rate of PCE - metabolism ^ CVL is venous
Concentration leaving the liver, KM is apparent Michaelis-Menten constant, KF is the first order
rate of metabolism):
dAL/dt = (QL(CA-CVL)-RAM)
Quite analogous, for mammary glands compartment the ^^^'o
contains a loss term for elimination for PCE from milk to pups, RPUP (equal to CMAT OU
wheTcMAT is concentration in milk, OUTX is periodic zero order milk yield per dam).
dAMAT/dt = (QMT(CA-CVMT)-KPUP)
where QMT represents mammary blood flow, CVMT represents ve
•4
-------
Oann
ct
OP
QC
cv
Afoeoli
Lung Blood
•+-CX
pat Tissue
Slowly Perfused
OF
OS
OR
Rapidly Perfused
CA
RUR
Metabolites
Fig. 1. Scheme of physiologically based pharmacokinetic model (Pb-Pk) used to simulate
lactational transfer of tetrachloroethylene (PCE) in nursing rats.
Abbreviations: CI = concentration in inhaled air (mg/L); QP = alveolar ventilation rate
adjusted for body weight (L/h); CX = concentration in exhaled air (mg/L); QC = cardiac
output adjusted for body weight (L/h); CVF = venous concentration leaving the fat tissue
(mg/L); QF = blood flow to fat (L/h); CVS = venous concentration leaving the slowly perfused
tissues (mg/L); QS = blood flow to slowly perfused tissues (L/h); CV = concentration in mixed
venous blood (mg/L); CA = concentration in arterial blood (mg/L); CVR = venous
concentration leaving the rapidly perfused tissues (mg/L); QR = blood flow to rapidly perfused
tissues (L/h); CVL = venous concentration leaving the liver tissue (mg/L); QL = blood flow
to liver (L/h); QPP = alveolar ventilation rate in pups adjusted for body weight (L/h); CXP =
concentration in air exhaled by pups (mg/L); CVMT = venous concentration leaving the
mammary glands tissue (mg/L); CMAT = concentration in milk (mg/L); QCP = cardiac output
in pups adjusted for body weight (L/h); CVP = concentration in venous blood in pups (mg/L);
CAP = concentration in arterial blood in pups (mg/L); RPUP = elimination rate for PCE from
milk to pups (mg/h); KFC = first order metabolism rate constant (1/h/kg); VMAX = pseudo-
maximal velocity of PCE metabolism (mg/h); KM = apparent Michaelis-Menten constant for
PCE metabolism (mg/L); AM = amount of PCE metabolized (mg); RMR = the rate of
gastrointestinal tract loading with PCE in pups (mg/h); RAP = the rate of gastrointestinal
absorption of PCE in pups (mg/h).
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TABLET. KINETIC CONSTANTS AND
PARAMETERS USED IN PB-Pk MODELING OF
TRANSFER OF TETRACHLOROETHYLENE IN RATS
HUMANS
Tissue Volumes
Maternal
Liver
Fat
Mammary
Perinatal
Pup (Infant) Tissue
Maternal
Slowly Perfused
Rapidly Perfused
Milk Volume
Flow Rates
Maternal
Alveolar Ventilation
Cardiac Output
Perinatal
Alveolar Vent. Pup (Inf
Cardiac Output Pup (Inf
Maternal
Liver
Fat
'[Fraction of Body WeigntT
VLC =0.04
VFC =0.1
VMATC= 0.044
VTCP =0.9
0.04
0.2
0.05
0.9
VS = 0.79*BW-VF-VMAT
VR = 0.12*BW-VL
VMILK= 0.00233 0.03542
[L/h/kg]
QPC
QCC
= 14.5
= 14.3
19.7
18.0
) QPCP =30.0
) QCCP =22.0
[Fraction of Cardiac Output]
25.2
22. .0
QLC
QFC
= 0.25
= 0.07
Partition Coefficients
Maternal
Blood/air
Liver/blood
Fat/blood
Slowly Perf./blood
Richly Perf./blood
Milk/blood
Perinatal
Blood/air Pup (Inf.)
Other Tiss./bld.Pup (Inf.)
Metabolism
[Ratio of Solubility]
PB
PL
PF
PS
PR
=33.
= 1.
=42.
= 0
= 1
5
9
35
93
67
PMILK=12.0
PPB
PPT
[mg/t,
Apparent Michaelis-Menten KM
* [mg/kg/h]
Pseudo Maximal Velocity ..I™*
[1/h/kg]
First Ord. Metab. Rate KFC
=24
= 4
.3
.54
- 0.32
°'°3
= 1.2
0.25
0.05
19.8
6.83
159.03
7
6
2
77
83
8
8.
6
0
596
0.32
0.151
1.2
-------
the mammary glands. Concentration, CMAT, equals to AMAT/VMILK where VMILK
represents volume of milk. It was assumed that the milk compartment is in intimate contact with
the arterial blood perfusing the mammary tissue, and that PCE rapidly equilibrates with the milk.
The rate of change in the amount of PCE in the pup's gastrointestinal tract (AGIT) is
described as a difference between the rate of ingesting of PCE with mother's milk (RPUP) and
the rate of absorption from the gastrointestinal tract, RAP (equal to MR*KAP; where MR is the
amount remaining in the gastrointestinal tract of pup, KAP is absorption constant for pup,
determinated to be equal to 0.5 1/hr):
dAGIT/dt = (RPUP-RAP)
The concentration of PCE in pup's gastrointestinal tract (CGIT) was calculated as MR/GIW,
where GIW represents weight of gastrointestinal tract of pup, adjusted for the pup's weight.
3.3. Closed Chamber Exposure
The closed chamber gas uptake data (Fig. 2) were used to estimate and optimize the
metabolism constants (VMAXC = 0.03 mg/kg/h; and KM = 0.32 mg/L). These values suggest
a very slow metabolism rate of PCE by lactating rats. Kinetic constants and physiological
parameters are listed in Table 1.
Computer simulations of gas uptake exposure to the initial air concentration of 670 ppm of
PCE for 6 h were conducted and predictions of the PB-Pk model are shown in Figure 3. The
predictions of blood and milk levels were compared to the results of measurement of respective
concentrations at the end of exposure (CV = 6.14±0.29 mg/L; CMAT = 89.18±12 mg/L; n
= 3).
3.4. Open Chamber Exposure
Further validation of the PB-Pk model was completed using lactating rats exposed for 2
hours to constant concentrations of PCE (ranging from 20jf2 to 1000.+47 ppm). The
dependence of PCE concentration in milk versus concentration in air (CI) is shown in Fig. 4.
The data for blood and milk collected from rats exposed to these different concentrations of PCE
were compared to the simulated levels by PB-Pk model. Validation of the dose-dependent model
predictions is shown in Fig. 5. Similarly, the data collected from lactating rats exposed for
different time to constant concentration of 600 ppm of PCE were compared to the levels in blood
and milk simulated by PB-Pk model. Validation of the time-dependent model predictions is
shown in Fig. 6. In both cases, the dose- and time-dependent courses of PCE concentrations
were in agreement with those predicted by the PB-Pk model.
3.5. Exposure of Pups via Mother's Milk
Another group of lactating rats was exposed for 2 hours using the same exposure
concentration of 600 ppm of PCE. The dams were next returned to their nursing pups and
concentrations of PCE were followed for the next 24 hours in blood and milk. Validations of
the time-dependent model predictions are shown in Fig. 7a and 7b, respectively. To achieve a
clearance of PCE from the systemic circulation at 26 hours, as indicated by the experimental
data presented in Fig. 7, a small first order term KFC was introduced (=1.2 1/hr/kg; see Table
I).
Concentrations of PCE were measured also in 10 to 11 day old pups for up to 24 hours
-------
Rat Lactation—Inhalation Gas Uptake
Rat Lactation—inhalation Gas Uptake
10*
300
1*4
Tim* (hours)
Fig 2 Results of gas uptake measurement (small rectangles) and
(solid ^) during exposure of lactating rats to eight different initial co
aS 1000 ppi of tetracMoroethylene (PCE) for 6 hours (n=3 for each data pornt).
Fie 3 Validation of PB-Pk model predictions (solid lines) of
ZStt~!2^KXtt3Z*-*'-. -
standard deviation (n=3).
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Rat Lactation—Inhalation
Rat Lactation—Inhalation, Dose-Dependent
MO
,£ 800
OJ . OJJ
C (10s) PCE Concentration (ppm)
0.4 &* 1£ I*
Time (hour*)
Z.4 2.8
Time (hours)
Fig. 4. Relationship between concentration of tetrachloroethylene (PCE), measured in milk
shortly after exposure, and the time weighted average PCE concentration in inhaled air for rats
exposed for 2 hours to 20 ppm to 1000 ppm of PCE (n=5 for each PCE air concentration).
Fig. 5. Validation of PB-Pk model predictions (solid lines) of tetrachloroethylene (PCE)
concentrations in blood (A) and milk (B) of lactating rats (small rectangles) exposed to different
constant concentrations, ranging from 20 ppm to 1000 ppm of PCE for 2 hours (n=5 for each
PCE air concentration).
9-
-------
Rat Lactation—Inhalation
10*
I w'
5j
I-CY ;
A "
Ul
Rat Lactation—Inhalation, Dam
1 i 4 § «
Time (hours)
Tlma (hours)
800
to if »
Time (hours)
30
Fig 6 Validation of PB-Pk model predictions (solid lines).of tetrachloroethylene (PCE)
concentrations in blood (A) and milk (B) of lactating rats (small rectangles) exposed to a constant
concentration of 600 ppm of PCE for 2 hours to 5 hours (n=5 for each exposure tome).
Fig 7 Validation of PB-Pk model predictions (solid lines) of time-dependent
tetrachloroethylene (PCE) concentrations in blood (A) and milk (B) of lactating rats (small
rectangles) exposed to 600 ppm of PCE for 2 hours (n=2 for each recovery tome).
10
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after the exposed dams were returned to feed them. Model validation of the time-dependent
predictions for pups is shown in Fig. 8 and 9. The nursing pup whole body burdening of PCE
was slightly underpredicted (Fig. 8a). On the other hand, PCE concentrations in venous blood
of pup were slightly overpredicted for times longer than 6 hours (Fig. 8b). From comparison
of the peak PCE concentration in a whole pup, including gastrointestinal tract (Fig. 8a), with
the peak concentration, of PCE in venous blood (Fig. 8b) it seems that the apex in blood
appeared several hours later than that in a whole pup. Even later than in blood, the apex of PCE
concentration appeared in solid pup tissues, other than gastrointestinal tract (Fig. 9a). On the
other hand, peak loading of the gastrointestinal tract (Fig. 9b) with PCE ingested by pups with
milk, appeared about five hours earlier than the peak in solid tissues. The loading of pup's
gastrointestinal tract and other solid tissues was slightly underpredicted by the model (Fig. 9).
4. DISCUSSION
4.1. PB-Pk Models in Lactational Transfer of Chemicals
The implementation of physiologically-based pharmacokinetic models by Shelley etal. (14,
17) represented significant progress in estimating the infant's exposure to chemicals transferred
with breast milk and assessment of the overall risk to the infant. Their approach involved a
physiologically-based mathematical simulation capable of modeling, for instance, the distribution
of volatile organic solvents from mother's breathing zone to the nursing infant. Such
physiologically-based pharmacokinetic models of lactational transfer of chemicals may be scaled
up or down, according to the body weight, and can be validated using laboratory animals, such
as lactating rats (15).
The same approach was used in the present study. However, the PCE distribution in dam
was better described by five compartments rather than three, as in the general PB-Pk model
presented by Shelley et al. (14, 17). On the other hand, the pup was tentatively described by
one, and finally by two compartments only, without incorporating metabolism. This was in
contrast to the PB-Pk model for trichloroethylene by Fisher et al. (15). Elimination of PCE in
the pup was assumed to occur by exhalation. The physiological parameters, partition coefficients
and metabolism parameters estimated or determined by experiments are shown in Table I. Using
these parameters, the PB-Pk model described fairly accurately both blood and milk
concentrations of PCE in lactating rats exposed to different concentrations of this chemical for
different periods of time (Fig. 3, 5, 6).
On the other hand, differences between the kinetics of loading pup's gastrointestinal tract
and blood or solid tissues required a separation of the gastrointestinal tract as a separate, initial
compartment which is loaded with PCE about 5 hour prior to the venous blood and solid tissues
(Fig. 8, 9). Using these assumptions, the Pb-Pk model predicted fairly accurately and reliably
the distribution of PCE inhaled by dams and passed onto their nursing pups via breast milk.
4.2. Computer Simulation of Repetitive Exposures in Rats
Assuming 2 hours exposure to 600 ppm of PCE, five times per week, a prognosis was run
to predict the time-course of PCE concentration in rat's milk for one month (Fig. lOa). With* the
same model settings, the accumulated dose of PCE received by 8 pups was estimated to reach
as much as 85 mg within one month (Fig. lOb). Under these exposure conditions it seems very
likely that the concentration of PCE in pup's blood may reach 1 mg/L, which is a concentration
referred to by the American Conference of Governmental Industrial Hygienists (ACGIH) as the
11
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Rat Lactation—Inhalation, Pup Average
Rat Lactation—Inhalation, Pup Average
to ts 20
Time (hours)
0.0
10 15
Time (hour*)
18 15 »
Time (houra)
Fig. 8. Validation of PB-Pk model predictions (solid lines) of time-dependent
tetrachloroethylene (PCE) concentrations in tissues of whole pup (A) and venous blood (B) of
pups fed by the dams exposed to 600 ppm of PCE for 2 hours. The small rectangle and vertical
bars show mean ± standard deviation (n=3 or 6 for each recovery time).
Fig. 9. Validation of PB-Pk model predictions (solid lines) of time-dependent
tetrachloroethylene (PCE) concentrations in solid tissues (A) and gastrointestinal tract (B) of pups
fed by the dams exposed to 600 ppm of PCE for 2 hours. The small rectangle and vertical bars
show mean ±. standard deviation (n=3 or 6 for each recovery time).
12
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index of biological exposure (BEI) to the threshold limit concentration (TLV-TWA = 50 ppm)
for PCE inhaled by adult human subjects (18).
4.3. Computer Simulations and Prognosis of PCE Distribution in Humans
The attempt was made to scale-up the PB-Pk model and to test its predictions versus
available data for humans. Initially, a set of physiological parameters and kinetic constants,
pertinent to PCE in humans, was adopted from Ward et al. (10). The values describing human
milk and mammary glands compartment were calculated from data published for "Reference
Man" (19), and finally the constants were optimized over the literature data describing inhalation
exposures of human subjects to PCE (7-9, 20, 21). The final set of parameters and constants
is listed in Table I.
Using these parameters with the milk compartment turned off, and the exposure scenario
described by ACGIH (18), the computer simulations of PCE concentrations in blood (Fig. lla)
and exhaled air (Fig. lib) were run and compared to BEI values. The model slightly
underpredicted both blood and exhaled air PCE concentrations for human subjects, prior to the
last shift of the workweek (Fig. 11). Much better fit of the computer-simulated time-course was
obtained with the data reported by Fernandez et al. (20) for exhaled air of human subjects
exposed to 100 ppm of PCE for 1 hour (Fig. 12a) and 8 hours (Fig. 12 b). Similarly, the model
predicted accurately PCE concentrations in exhaled air of human subjects exposed to 194 ppm
of PCE for 90 min and 3 hours (Fig. 13), as reported by Stewart et al. (7). Fig. 14 shows
computer simulation of the rate of PCE exhalation (RAX) run versus data reported by.
Bolanowska et al. (21) for two human subjects, a slim man and an obese woman (Bolanowska,
personal communication). The model predictions of PCE exhaled breath clearance rates (RAX)
in the two subjects were in general agreement with modest overprediction of experimental data
after the first measured time point (Fig. 14).
4.4. Computer Simulations and Prognosis of PCE Distribution in Mother and Her Nursing
Infant
An attempt was made to simulate the only documented case of the lactational transfer of
PCE from mother to infant, described by Bagnell et al. (5). Although the PCE concentration in
inhaled air was not measured the reported incidents of dizziness after exposure of mother to PCE
without symptoms of general anesthesia (5) suggested the PCE air concentration within the range
of several hundreds ppm. The best approximation of the computer simulated values to PCE
•concentrations determined in blood and breast milk (5) was achieved when the exposure
concentration in inhaled air was assumed to be 600 ppm (Fig. 15a, b). This concentration,
exceeding more that ten times the air TLV-TWA level recommended by ACGIH (18) for PCE,
could result in the infant blood concentration of not more than 0.035 mg/L within one month
of exposure to PCE via mother's milk. This concentration is more than one order of magnitude
lower than the no-effect threshold assumed for adults by ACGIH (18).
From these several prognoses of PCE distribution and its kinetic behavior in exhaled air,
blood and milk of exposed human subjects and especially from the comparison of computer
simulations with the available human data from literature, it is concluded that the PB-Pk model
described fairly accurately the concentrations of PCE in both lactating rats and humans. It
seems that the validated PB-Pk model may be used, consequently, to predict the absorbed doses
of PCE by nursing infants from the concentration in mother's breathing zone. Although this
approach will require monitoring of concentrations of PCE and other volatile chemicals in the
13
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Rat Lactation—Inhalation, Pup Average
(00
-7 500
400
000
8
UJ
100
100
CMAT
100 200 300 400 500 600
Time (hour*)
B
Time (hours)
TOO
B
300 400 500 *00 TOO
30
I
Is
1
I
S 15
e
o
10
3
Ul
o
a.
ACGIH. TLV-BE1
25 SO
75 100 125 150 175
Time (hours)
/
....A
.CXPPM
25
15 7S 100 125
Time (hours)
Fie 10 Simulatedprognosisoftime^ependentconcentrationsoftetrachloroethylene(PCE)
raSVuk (^Sumulated doses of tetrachloroethylene (PCE) received by 8 pups wi*
iS from Ae dams (B). The computer simulation was run assuming 2 hours ^sure of dams
of PCE, five times per week (Monday through Friday, beguung on Fnday), during
to
1 month.
Fig. 11. Simulatedprognosis
frreflmeipet week for 8 hours, Monday through Friday, beguung on Monday).
i^ lowSgical exposure mdie« (BED in blood (A) and exhaled a* (B). Data
according to (18).
14
-------
Fernandez et al. 8 Hours Exposure to 100 ppm of PCE Stewart et al. Exposure: ••-90 Minutes; •—3 Hours
A "I '
30
Ul.
o
a.
5 eo
I
I
S 40
2
i
20
Ul
O
a.
• • = CXPPM
10 12
Time (hours)
3 12 16 20 24
Time (hours)
Bolanowska et al. Subject: •—Obese Woman; •—Slim Man
100
Fernandez et al. 1 Hour Exposure to 100 ppm of PCE
B 4U
Exhaled A
is
c
g
|
§ 10
UJ
^
D 1
; B= CXPPM
• i :
1 ' | '"]
234!
12 16
Time (hours)
Time (hours)
Fig. 12. Simulated prognosis of time-dependent concentrations of tetrachloroethylene (PCE)
in exhaled air of human subjects exposed to 100 ppm of PCE for 1 hour (A) and 8 hours (B),
according to the scenario reported by Fernandez et al. (20). Small rectangles show data in
exhaled air according to (20).
Fig. 13. Simulated prognosis of time-dependent concentrations of tetrachloroethylene (PCE)
in exhaled 'air of human subjects exposed to 194 ppm of PCE for 1.5 and 3 hours, according
to the scenario reported by Stewart et al. (7). Small rectangles show data in exhaled air
according to (7).
Fig. 14. Simulated prognosis of time-dependent rates of tetrachloroethylene (PCE)
exhalation by obese woman and slim man, exposed to 55 ppm of PCE for 6 hours, according
to the scenario reported by Bolanowska et al. (21). Small rectangles show data in exhaled air
according to (2-1). --^~»-
15
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Bagnell et al. Nursing Mother Exposure to 600 ppm of PCE
"~
10
200 300 «0
Time (hours)
12 18
Time (hours)
'according to (21).
1 month, according to the scenario reported by Bagnell et al. (21).
16
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air, it would aid the future attempts of risk assessment for infants.
ACKNOWLEDGEMENTS
This work was performed under Department of the Air Force Contract #F33615-90-C-0532.
REFERENCES
1. M.V.Cone, M.F.Baldauf, D.M.Opresko, and M.S.Uziel, "Chemicals identified in human
breast milk, a literature search" (EPA 560/5-83-009 Report. US. Dept. of Commerce Natl
Technical Inform. Service, Washington, DC, 1983).
2. D.Giroux, G.Lapointe, and M.Baril, "Toxicological index and the presence in the workplace
of chemical hazards for workers who breast-feed infants," Am. Ind. Hyg: Assoc. J. 53
471-474.
3. A.A.Jensen, S.A.Slorach, "Chemical Contaminants in Human Milk" (CRC Press Inc Boca
Raton, FL, 1991).
4. Committee on Drugs, American Academy of Pediatrics, "Transfer of drugs and other
chemicals into human milk," Pediatrics 84, 924-936 (1989).
5. P.C.Bagnell, and H. A.Ellenberger, "Obstructive jaundice due to a chlorinated hydrocarbon
in breast milk," Can. Med. Assoc. J. 117, 1047-1048 (1977).
6. R.D.Stewart, "Acute tetrachloroethylene intoxication," J. Amer. Med. Ass. 208 1490-1492
(1969).
7, R.D.Stewart, E.D.Baretta, H.C.Dodd, and T.Torkelson, "Experimental human exposure to
tetrachloroethylene," Arch. Environ. Health 20, 224-229 (1970).
8. R.D.Stewart, D.S.Erley, A.W.Schaffer, and H.H.Gay, "Accidental vapor exposure to
anesthetic concentrations of a solvent containing tetrachloroethylene," Ind. Med Surg 30
327-330(1961). '
9. R.D.Stewart, H.Gay, D.Erley, C.Hake, and A.Schaffer, "Human exposure to
tetrachloroethylene vapor," Arch. Environ. Health 2, 516-522 (1961).
10. R.C.Ward, C.C.Travis, D.M.Hetrick, M.E.Andersen,.andM.L.Gargas, "Pharmacokinetics
of tetrachloroethylene," Tox. Appl. Pharmacol. 93, 108-117 (1988).
11. M.L.Gargas, M.E.Andersen, and H.J.Clewell,m, "A physiologically based simulation
approach for determining metabolic constants from gas uptake data," Toxicol Appl
Pharmacol. 86, 341-352 (1986).
12. M.L.Gargas, RJ.Burgess, D.E.Voisard, G.H.Cason, and M.E.Andersen, "Partition
coefficients of low-molecular weight volatile chemicals in various liquids and tissues,"
Toxicol. Appl. Pharmacol. 98, 87-99 (1989).
13. J.C.Ramsey, and M.E.Andersen, "A physiologically based description of the inhalation
pharmacokinetics of styrene in rats and humans. Toxicol. Appl. Pharmacol. 73. 159-175
(1984).
14. M.L.Shelley, M.E.Andersen, and J.W.Fisher, "An inhalation distribution model for the
lactating mother and nursing child," Toxicol. Lett. 43, 23-29 (1988).
15. J.W.Fisher, T.A.Whittaker, D.H.Taylor, H.J.Ciewell,m, and M.E.Andersen,
"Physiologically based pharmacokinetic modeling of the lactating rat and nursing pup: A
multiroute exposure model for trichloroethylene and its metabolite, trichloroacetic acid.
Toxicol. Appl. Pharmacol. 102, 497-513 (1990).
16. J.Z.Byczkowski, E.R.Kinkead, R.J.Greene, L.A.Bankston, and J.W.Fisher,
17
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"Physiologically-based modeling of the lactational transfer of tetrachloroethylene,"
.
(1972).
"inhalation
experimental conditions," Medycyna Pracy 23,
1-8
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Biographical Sketch: MAJ(P) Daniel J. Caldwefl, Ph.D., U.S. Army
MAJ Caldwell received his undergraduate degree in Environmental Health from East
Tennessee State University in 1976. He also holds a M.S. in business administration
from Boston University Metropolitan College (1981) and a M.H.S. in Occupational
Safety and Health from the Johns Hopkins University School of Hygiene and Public
Health (1984). His Ph.D. in Toxicology was received from the University of Pittsburgh
in 1991. MAJ Caldwell is certified as a Diplomate of the American Board of Industrial
Hygiene.
Since May of 1991 he has served as Chief of the Toxicology and Exposure
Assessment Section, Occupational Health Research Detachment, of the U.S. Army
Biomedical Research and Development Laboratory. He is responsible for health
hazard and exposure assessment, development of a combustion toxicology database,
and formulation of permissible'exposure standards for military unique compounds or
substances for which there are no federal standards. MAJ Caldwell has also been
assigned to the Army Environmental Hygiene Agency and the Ft. Meade Medical
Department Activity, where he was responsible for health hazard assessments and
environmental and occupational health programs at numerous Army installations. He
has authored/co-authored .several publications, a chapter in a medical text book, and
two Army Technical Bulletins. MAJ Caldwell is an Adjunct Assistant Professor of .
Engineering and Environmental Management at the U.S. Air Force Institute of
Technology.
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Issues in the Development of a Risk Assessment for Fire Hazards
MAJ(P) Daniel J. Caldwell, Ph.D.
In general terms, the risk assessment process determines information on the
nature and extent of a hazard, as differentiated from the risk management process which
encompasses judgements of acceptability and determination of degree to which the risks
should/can be controlled [1]. Risk assessment is primarily scientific, while risk
management takes into consideration other factors such as cost, technicai feasibility, and
timing.
Numerous hazards to humans result from exposure to fire conditions and the
products of combustion. Predominant among these are effects from heat and flames,
visual obscuration due to the density of smoke or to eye irritation, narcosis from inhalation
of asphyxiants, and irritation of the upper and/or lower respiratory tracts [2]. These
effects often occur simultaneously in a fire and contribute to physical incapacitation, loss
of motor coordination, faulty judgement, disorientation, restricted vision, and panic. The
resulting delay or prevention of escape may lead to subsequent injury or death from
further inhalation of smoke.
Assessing the effects of exposure of humans to smoke is extremely difficult since
smoke is a continuously changing mixture of airborne solid and liquid particles and gases
which is evolved when a material undergoes pyrolysis or flaming combustion. The toxicrty
of the smoke produced is therefore time-dependent [3,4]. This dictates that a dynamic
(i.e., flow-through) animal exposure system be employed to properly evaluate the toxicity
-------
of the smoke [5]. To fully assess fire hazard, one must evaluate the inherent material
properties that influence flannmability and flame spread and integrate this information with
toxicity data obtained from a combustion test method which replicates likely real-world
exposures [6,7]. The two variables affecting the toxicity of combustion products are the
yield of toxicants and the tirne-to-effect [6]. Historically, death has been the endpoint of
choice for evaluation of the toxic potency of combustion products [4,7]. However, this
does not account for the time to effect.
To minimize risk from fire hazards, emphasis must be placed on SURVIVABLE
EXPOSURES rather than lethality. The survival time is measurable and dependent upon
two variables: potency of each toxicant evolved as well as their rapidity of action. Burning
conditions influence both the evolution of toxicants and behavior of a material under fire
conditions. Thus, the inherent chemical/physical properties which determine flammability
also effect the toxic hazard of a material. Because of this, toxicity of smoke can not be
predicted a priori by simple additive models based on the fractional effective dose
approach (e.g., the N-gas model, which while suitable for multiple .gas exposures it fails
to account for the effects due to smoke exposure).
Fortunately, however, a methodology exists which has been proven to predict
toxicity due to smoke exposure [5]. This method is an evolutionary development in the
field of combustion toxicology which allows, for the first time, an evaluation of materials
under well defined burning conditions over a wide range of heat flux and ventilation levels.
The toxic hazard of the smoke produced was influenced by the burning conditions to
which the materials were exposed.
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• A Potential Smoke Hazard index was developed to integrate the material
performance characteristics, which determine ease of ignition and flame spread, with the
toxic hazard (i.e., toxic potency, time to toxic effect, and rate of generation of toxicants).
Not only is the toxicity of the smoke described in terms of toxic potency (i.e., LC50), but
the time, to toxic effect and fundamental material properties that influence the burning
behavior are also determined [6].
Furthermore, the influence of a given set of burning conditions on the time to toxic
effect, e.g., death or incapacitation, has never been evaluated in detail [2]. Hazardous
concentrations of smoke develop in a fire prior to "flashover", thus it is important to
determine at what time an untenable condition occurs. The time to manifestation of a
toxic effect is measurable and dependent upon two variables: 1) the rate of evolution of
toxicants from the burning material, and 2) the potency and rapidity of action of each
toxicant generated. The burning conditions directly influence the rate of toxicant
evolution.
Three fundamental variables are needed to describe the burning conditions of real
fires. These are the imposed heat flux, or irradiance, the ventilation, and the mass loss
rate [5]. Previous small-scale methods to evaluate toxicity of smoke are restricted to one
set of conditions in each apparatus used. In order to evaluate the effects of burning
conditions on smoke toxicity, a small-scale flaming combustion apparatus was developed.
The UPITTII apparatus, which has been described previously in great detail, is unique in
that it permits control of both heat flux and ventilation, and measurement of the mass loss
rate of the burning specimen [5],
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As discussed above, the time to effect plays a major part in determining the toxic
hazard of smoke. Although the toxic potency of a material changed very little over the
range of burning conditions investigated, the time to effect was greatly influenced by the
burning conditions. The relationship between the median time to death and the smoke
concentration was investigated to obtain an estimate of the time necessary to produce
an effect starting at the beginning of heat flux to the specimens.
Material performance data have not been considered, except the mass loss rate
[4]. It is clear that material performance should be included in the risk analysis since the
stability of some materials precludes ignition at lower imposed heat flux levels; yet this fact
is usually neglected, and taking toxic potency alone is not appropriate when addressing
the total hazard from fires. The hazard analysis must include material performance as.
well [5,6]. The time to ignition (Tign) for a material at a variety of heat flux levels yields
data from which the critical flux (q"cr), which is the flux below which no ignition or pyrolysis
will occur, and thermal inertia (Tl) of a material, can be determined [5]. These variables
can then be taken into account to calculate an index of potential smoke hazard (PSH) [6].
The PSH index incorporates the variables, q"cr and Tl, which determine "ease of
ignition" of a material and rate of fire development or flame spread, the toxic potency and
time to effect, and the rate of generation of toxicants from the mass loss rate (m).
The PSH index can be modified to permit calculation of an index based on survivability
data obtained from studies using such alternative endpoints, while retaining the material
performance characteristics determined with this method [8]. Such an index would be
a valuable tool to .assist in materials selection decisions.
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f NSAcademy of Sciences (1983). Risk Assessment ir .the Tedera, Government:
Managing the Process. National Academy Press, Washington, DC. pp. 1^1.
2. National Research Councii, Committee on Fire ^c^(^6.e and Smoke:
Understanding the hazards. National Academy Press, Washington, DC.
3 International Organization for Standardization (1992). Technical Report TR-9122, Part
4: The Fire Model, ISO/TC92.
4 International Organization for Standardization (1992). Technical Report TR-91 22, Part
5: Prediction of toxic effects of fire effluents, ISO/TC92.
(1990).
iomcs^^^
UPfc U Fiamfng Combustion/Toxicity of Smoke Apparatus. J. of F.re Sciences 9, 470-518,
(1991).
7 Purser D A 1992. "The harmonization of toxic potency data for materials obtained
OR September 1992. Interscience Commumications, LTD, Lonaon.
"Application of the UPitt
39th
n
decisn:
method to materials selection
Research^fereDc
14-18 September. 1992. in press
The views opinions, and/or findings contained in this report are those of the
authored should not be construed as official Department of the Army poabon, pohcy,
or decisTon, unless so designated by other official documentor,.
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BIOGRAPHICAL SKETCH: HARVEY J. CLEWELL, III
Harvey J. Clewell, III is currently Senior Project Manager and
Director of the Health Assessment Group for the K.S. Crump Division
of ICF Clement International. in this capacity, he conducts and
directs research to assess the health risks associated with toxic
chemicals, as we'll as to advance the state of the art of chemical
risk assessment. He recently retired from the Air Force as a
Lieutenant Colonel; during his Air Force career he performed
research in environmental modeling and in physiologically based
pharmacokinetic modeling for application to chemical risk
assessment. His positions in the Air Force included Deputy
Director of the Toxic Hazards Division (the Air Force's toxicology
unit) and Director of Hazardous Materials Safety for Aeronautical
Systems Division. He also served as Consultant to the Air Force
Surgeon General for chemical risk assessment.
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INCORPORATION OF PHARMACOKINETICS IN NON-CARCINOGENIC RISK ASSESSMENT:
EXAMPLE WITH CHLOROPENTAFLUOROBENZENE
Harvey J. Clewell IIP and Bruce M. Jarnotb
a K.S. Crump Division, ICF International, Ruston, Louisiana
' " Toxicology Division, Armstrong Laboratory, Wright-Patterson Air Force Base, Ohio
ABSTRACT
Non-carcinogenic risk assessment traditionally relies on applied dose measures, such as.
concentration in inhaled air or in drinking water. Safety factors are then incorporated to address the
uncertainties associated with extrapolating across species, dose levels, and routes of exposure, as well
as to account for the potential impact of variability of human response. A risk assessment for
chloropentafluorobenzene (CPFB) was performed in which a physiologically based pharmacokinetic
(PBPK) model was employed to calculate an internal measure of effective tissue dose appropriate to
each toxic endpoint. The model accurately describes the kinetics of CPFB in both rodents and
primates. The model calculations of internal dose at the no-effect and low-effect levels in animals
were compared with those calculated for potential human exposure scenarios. These calculations were
then used in place of the default safety factors to determine safe human exposure conditions.
Estimates of the impact of model parameter uncertainty and human pharmacokinetic variability, as
estimated by a Monte Carlo technique, were also incorporated into the assessment. The approach
used for CPFB is recommended as a general methodology for non-carcinogenic risk assessment
whenever the necessary pharmacokinetic data can be obtained.
INTRODUCTION
For a number of years the U.S. Air Force has been performing research to develop safe
intake simulants for chemical warfare agents, in order to provide accurate and quantitative real-time
assessment of troop proficiency and gear efficacy during chemical warfare field exercises.
Chloropentafluorobenzene (CPFB) was identified and evaluated as a candidate inhalation simulant,
and was determined to possess desirable physicochemical and toxicological properties. These include
rapid uptake, low metabolism and toxicity, rapid and predictable clearance, real-time detectability by
existing portable "breathalyzer" technology, gas mask breakthrough similar to the actual agents, and
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commercial availability. Before using CPFB in human trials, it was important to determine safe
exposure conditions, taking into consideration the exposure levels at which toxicity was observed in
animal studies.
Because of the need to balance protection of personnel during training with the ability to
provide effective training for a dangerous wartime scenario, an accurate (as opposed to simply safe-
sided) estimate of acceptable human exposure was needed.' The usual practice for non-carcinogenic
risk assessment (Barnes and Dourson, 1988) uses measures of applied dose to relate to toxicity.
Safety factors are then applied to account for uncertainty regarding the relationship between applied
dos and effective target tissue dose across routes of exposure and species, as well as for variability in
the human population. A more scientifically based approach would be to use a measure of tissue dose
directly and to use known principles of pharmacokinetics to relate different exposure scenarios. For
this purpose a physiologically based pharmacokinetic (PBPK) model was developed which could be
used to perform the route-to-route and cross-species extrapolations necessary to develop a human risk
estimate. The model was also used in a Monte Carlo analysis to estimate the uncertainty and
variability associated with the risk estimate.
PBPK MODEL .DEVELOPMENT
Structure
The structure of the model is shown in Figure 1, and the assumptions underlying the
mathematical description follow those of Ramsey and Andersen (1984) with the following exceptions:
1. The model of Ramsey and Andersen included only saturable metabolism. An additional
pathway of metabolism has been added in this model which is linear in concentration. Thus
the equation for the rate of change of amount of CPF.B in the liver contains an additional term:
- KF * CL * VL / PL
(where the parameters are defined in Table 1)
2. A GI tract compartment has been added. Oral absorption takes place into this compartment
by a first order process: KA * AST (where AST represents the amount of CPFB remaining in
the stomach). The liver receives the blood flow from this compartment (QG) as well as its
own arterial supply (QL). Thus the equations for the rate of change in the amount of CPFB in
the stomach (RAST), GI tract (RAG), and the liver (RAL) are:
-------
RAST = - KA * AST
RAG = QG * (CA - CG / PG) 4- KA * AST
RAL = QG * (CG / PG - CL / PL) + QL * (CA - CL / PL)
- VMAX * CL / PL / (KM + CL / PL) - KF * CL * VL / PL
3. A bone marrow compartment has been added. The form of the equation for the rate of
change in the amount of CPFB in the bone marrow (RAM) is identical to that of the other
basic tissues in Ramsey and Andersen (e.g., fat, slow, rapid):
RAM = QM * (CA - CM / PM)
4. In order to better simulate the measurements of exhaled breath in anesthetized monkeys,
the description of gas exchange between the lung and the blood was modified to explicitly
model an alveolar space in which inhaled air at concentration CI and air in equilibrium with
the blood were mixed. In place of the steady-state assumption used in Ramsey and Andersen,
the following rate equation for the amount of CPFB in the blood (ABL) was integrated along
with the equations for the tissue compartments:
RABL = QP * (CALV - CX) + QC * (CV - CA)
where:
CALV = ALVS * ABL / (VBL*PB) + (1. - ALVS) * CI
The measured exhaled air concentration in parts per million (CXPPM) was then described by
the equation:
CXPPM = [DS * CI 4- (1. - DS) * ABL / (VBL * PB)] * 24450. / 202.51
the parameters for the model are shown in Table 1. Physiological parameters for mouse, rat,
and human were developed from literature data collected (Stan Lindstedt, Northern Arizona
University, personal communication) as part of an ongoing Physiological Parameters Work Group
effort sponsored by the ILSI Risk Science Institute . Physiological parameters and partition
coefficients for the rhesus monkey were adapted from Crank and Vinegar (1992). Partition
coefficients for the rat and mouse were taken from Jepson et al. (1985). Partition coefficients for
humans were assumed to be the same as for monkeys. Metabolism was modeled as a first-order
process, scaled allometrically from the value determined in rats (Jepson et al., 1985). The model was
written in the Advanced Continuous Simulation Language (ACSL, Mitchell and Gauthier, Boston
MA) and was compared with experimental data using SimuSolv (Dow Chemical Co., Midland MI).
The Monte Carlo analysis was performed on the ACSL model with PBPK_SIM (K.S. Crump Div.,
ICF Int., Ruston LA).
-------
Figure 2 shows the results of gas uptake analysis of CPFB in rats (Jepson et al., 1985). In
the gas uptake analysis, several animals are maintained in a closed chamber, and the air is
continuously recirculated. Oxygen is replenished and carbon dioxide is scrubbed as necessary to
maintain stasis. A known, amount of a volatile chemical is then added to the chamber, and the
concentration of the chemical in the chamber is monitored over time. The rapid initial decline in the
chamber concentration of CPFB seen in Figure 2 is due to uptake by the animals' tissues and
demonstrates that CPFB is readily absorbed. Following the tissue uptake phase, any further decline
in chamber concentration would indicate loss of chemical due to metabolism. The fact that the
concentration curve for CPFB almost levels out after the first few hours reflects the.fact that CPFB is
not extensively metabolized. By way of comparison, the chamber concentration of a more rapidly
metabolized chemical, bromopentafluorobenzene, decreased by more than 20% between hours 3 and 6
under the same conditions. Using the PBPK model for CPFB, the closed chamber data was analyzed
to quantify the rate of metabolism. It was determined that metabolism was first-order, with a rate
constant of 2/hr (scaled to a 1 kg animal by body weight to the -0.25 power).
Model Validation
As a test of the model, a study was simulated in which rats were exposed 6 hours per day by
inhalation for 21 days to CPFB at 30, 100, and 300 ppm (Kinkead et al., 1990a). Figure 3 shown the
measured and simulated venous blood concentration of CPFB for the eleventh day of the exposure.
As a further evaluation of the model, inhalation exposures to CPFB on eight anesthetized rhesus
monkeys (Crank and Vinegar, 1992) were simulated. In these experiments CPFB concentrations in
expired breath were measured during and after 15 minute exposures at 300 ppm. The PBPK model
was evaluated in terms of its ability to relate exposure concentration and exhaled-air concentrations.
The results are shown in Figure 4.
TOXICOLOGICAL EVALUATION
In order to assure that CPFB could safely be used as an intake simulant, a number of studies
were performed to evaluate its potential toxicity. These studies were designed to elucidate any
short-term or long-term effects, and to assess the likelihood that CPFB could be carcinogenic or
teratogenic.
Acute Toxicitv
-------
Hie primary irritation hazard, sensitization potential, and acute inhalation toxicity of CPFB
were evaluated by Kinkead et al (1987). CPFB demonstrated no potential for skin sensitization in
tests on guinea pigs, and was only a mild skin and eye irritant in rabbits. Short-term exposure to
CPFB vapor poses no serious hazard by the inhalation route as all rats survived a 4-hour exposure to
an upper limit concentration of 4.84 mg/L (581 ppm), a concentration many orders of magnitude
higher than that which is likely to be encountered in the field. Similarly, oral dosing indicates an
LD* of greater than 5 g/kg, which would classify CPFB as "practically non-toxic" (Kinkead et al.,
1990b).
Mutagenicitv/Genotoxicitv
CPFB was tested for potential genotoxic activity by three different laboratories CTu et al., .
1986; Steele, 1987; Kutzman et al., 1990) using a battery of in vitro assays (Table 2). The first
attempt to perform these assays CTu et al., 1986) was compromised by experimental difficulties
associated with the tendency of CPFB to precipitate out of solution and to dissolve the dishes. In the
second study (Steele, 1987), it was again noted that CPFB dissolved the standard plastic dishes, so the
study was performed in specially designed glass dishes. A third study (Kutzman et al:, 1990) was
performed by a reference laboratory since the results of the first two studies seemed to be somewhat
equivocal.
.CPFB does not appear to be mutagenic. The Ames Salmonella reverse mutation assay was
uniformly negative in all studies, both with and without the addition of a rat liver S9 metabolic
activation system. Similarly, all of the laboratories obtained negative results when CPFB was tested
in mammalian cell culture for mutagenic activity at the HGPRT locus in Chinese hamster ovary cells.
The results of tests for genotoxicity were less consistent. There was some evidence of
CPFB-induced sister chromatid exchange and/or chromosomal aberration in the earlier studies, but the
final study detected no increases in chromosomal aberrations and only observed sister chromatid
exchange with the addition of liver S9 metabolic activation (suggesting that generation of significant
levels of metabolite may be required to observe this effect). In the case of the assay for unscheduled
DNA repair synthesis in primary rat hepatocytes, the first study suggested that CPFB produced
increased repair of DNA damage; however, both the second and third studies failed to confirm this
finding. Cell transformation results were also variable, with only the second study showing any
-------
indication of an ability of CPFB to induce morphological transformation in vitro in BALB/C-3T3
cells.
To resolve the question of whether CPFB could act as a genotoxic or cytotoxic agent under
in vivo conditions, a 21-day exposure of mice to CPFB at 30, 100, and 300 ppm was performed
(Kinkead et al., 1989). Under these conditions CPFB did not induce an increase in sister chromatid
exchange in the bone marrow of the exposed mice, and the rate of cellular proliferation in the bone
marrow was not altered. Similarly, assessment of the micronucleated polychromatic and
normochromatic erythrocyte populations during the exposures indicated a general absence of
genotoxic activity. A PBPK model for CPFB was used to assess the tissue exposure to CPFB during
this study (Kinkead et al., 1990a). Based on the modeling, bone marrow tissue exposure to CPFB
during the in vivo study was similar to or greater than the concentrations used in the in vitro assays.
The PBPK model described in this paper was used to reconfirm the results of this earlier
analysis in the particular case of sister chromatid exchange. A dose-related increase in sister
chromatid exchange was observed -for 2-hr in vitro exposures to CPFB ranging from 100 to 250 mg/L
(area under the curve ranging from 200 to 500 mg/L-hr) in the presence of metabolic activation. For
the in vivo study, bone marrow exposure to CPFB (as estimated by the model described in the
Appendix) averaged 288 mg/L during the daily 6-hr inhalation exposures to 300 ppm CPFB, with a
daily area under the curve in the marrow of 2023 mg/L-hrs. The lack of in vivo response appears
therefore to reflect differences between the in vivo and in virro situation rather than failure to achieve
sufficient tissue exposure levels. It is possible that the bone marrow does not possess sufficient
metabolic activity, in comparison with the in vitro situation, to generate the active chemical species.
Full evaluation of the potential for CPFB to be carcinogenic would require a lifetime animal
bioassay. However, a reasonable assessment of the likelihood that CPFB could act as a carcinogen.
can be made on the basis of the above results, taken together with the rather unremarkable results of
the subchronic exposures. CPFB does not appear to be mutagenic, either in the presence or absence
of metabolic activation, and the questionable in vitro suggestions of genotoxicity were not born out by
the in vivo studies. In addition, subchronic exposure (Kinkead et al., 1990b) did not produce any of
the tissue changes, such as peroxisomal proliferation, which typically accompany promotional
carcinogenesis in rodents. Therefore, it is not likely that CPFB would be carcinogenic, even under
the conditions of a lifetime bioassay.
-------
frihacute and Suhchronic Toxicitv
Repeated exposure of rats to high concentrations of CPFB produced lethargy and
incoordination (1000 ppm, 6 hours/day, 4 days) or unresponsiveness (500 ppm, 6 hours/day,
15 days), but no tissue pathology (Gage, 1970). No behavioral or historical effects were observed
for exposure to 250 ppm, 6 hours/day, for 15 days (Gage, 1970). [Note: Gage (1970) incorrectly
shows the concentration of the lowest exposure level as 50 ppm; the original ICI report, TR/449,
records the concentration as 250 ppm - J.C. Gage, personal -communication.]
In a more recent study (Kinkead et al., 1989), ten Fischer-344 rats and six B6C3F1 mice of
each sex were exposed to 30, 100, and 300 ppm CPFB for 3 weeks (15 exposures). Exposure to the
highest concentration caused a reduction in the growth rate of rats, but did not affect the growth rate
of mice. Both rats and mice showed a dose related increase in liver to body weight ratios. Mice
showed clear evidence of liver toxicity (hepatocytomegaly and hypertrophy) at the highest exposure
concentration. Another treatment-related change in the livers of male and female mice and female
rats was an increase in the incidence of single-cell necrosis in all CPFB-exposed groups. The
formation of hyaline droplets in the kidneys of male rats was also noted, but the severity of the lesion-
was minimal, and no other kidney effects were seen. Consistent with the earlier study, no behavioral
effects were noted, even at the highest dose.
In order to better evaluate the impact of prolonged or repeated exposure to CPFB, as well as
to determine a no-observable-effect level, a 13-week exposure of rats and mice was carried out at
concentrations of 1.2, 6, and 30 ppm (Kinkead et al., 1990b; 1991). No treatment-related effects
were observed at any concentration in either species. In particular, the single cell necrosis seen in the
3-week study at 30 ppm was not observed in the 13-week study at the same concentration. A review
of the tissues from the earlier study confirmed the finding of an increase over control, but both the
number and severity of the lesions were so slight that ifwas felt the finding was biologically
unimportant. Thus the only adverse effects seen were those noted for the 300 ppm exposure
concentration in the 3-week study. A concentration of 30 ppm was therefore recommended by the
investigators as a no-effect level in humans to protect individuals subjected to repeated inhalation of
CPFB for extended periods.
Reproductive Toxicitv
-------
To evaluate the teratogenic potential of CPFB, time-mated Sprague Dawley rats were dosed
orally at 0.3, 1.05, and 3.0 g/kg/day on days 6 through 15 of pregnancy (Copper and Jarnot, 1992).
There was a significant reduction in maternal body weight and a significant increase in maternal liver
weight at the highest dose. The percentage of post-implantation fetal loss was also greater only at the
highest dose. Fetal weight and length differed significantly from the controls at both the high and
intermediate doses, indicating a slightly increased fetotoxicity compared to the dam. The number of
malformations and variations observed at any of the doses did not differ from controls, suggesting
that CPFB is not teratogenic.
Metabolism
Studies of the uptake of CPFB in a closed, recirculated chamber were consistent with a slow
rate of first order metabolism (Jepson et al., 1985). In the same studies, the rate of metabolism of
the related compound, bromopentafluorobenzene, was unaffected by pretreatment with the potent
P450 inhibitor, pyrazole, suggesting that metabolism of these two compounds is not associated with
the mixed function oxidase system. This finding contrasts with the metabolism of the related
compound, hexachlorobenzene (HCB), which is characterized by both an oxidative (P450) pathway
and a glutathione conjugation (GST) pathway (Renner, 1988). This apparent difference between
CPFB and HCB is consistent with the results of a comparative study of a series of dihalomethanes
(Gargas et al., 1986), which also feature competitive P450 and GST metabolism. This study
demonstrated that the fluorine-substituted congeners, CH2F2 and CH2FC1, showed little evidence of
P450 activity, whereas compounds containing chlorine and/or bromine, but not fluorine, were readily
metabolized by both pathways. Of course, these results were observed in rodents, and the possibility
of species differences in the metabolism of CPFB cannot be ruled out. Evaluation of CPFB
metabolism in human tissues would be necessary to confirm the assumption of equivalent metabolism
across species.
In the case of HCB, the GST pathway initially produces N-acetyl cysteine conjugates which
cleave to form chlorothiophenols, which are in turn subject to further metabolism (Renner, 1988). It
can therefore be hypothesized that the liver toxicity associated with repeated exposure to CPFB may
result from the generation of the analogous metabolite, pentafluorothiophenol (PFTP), a toxic
compound with an LD^, of 56 mg/kg (NIOSH, 1992).
-------
EXPOSURE GUIDELINE DETERMINATION
The critical effect for evaluation of safe exposure to CPFB is the liver toxicity associated with
repeated exposure (Kinkead et al., 1990a). Specifically, hepatocytomegaly and hypertrophy were
observed in mice following exposure to 300 ppm CPFB, 6 hours/day, for 3 weeks, and the liver to
body weight ratio in rats and female mice were increased in a dose related fashion. Increased single
cell necrosis was also observed at 30 ppm and 100 ppm in the same study, but this effect was not
considered theologically significant, and neither the necrosis nor the increased liver to body weight
ratio were reproduced in a subsequent study at 30 ppm for 13 weeks (Kinkead et al, 1991). In the
traditional approach, taking 30 ppm as a No Observed Adverse Effect Level (NOAEL),- adjusting for
the difference in daily exposure duration (6 hrs for animal studies, 8 hrs for humans), and dividing by
a factor of 33 to provide a margin of safety, yields a recommended exposure guideline of 0.7 ppm for
a daily (8-hour) time-weighted average.
The rationale for the factor of 33 used in the traditional guideline calculation is as follows.
First, the animal NOAEL must be adjusted for the relationship between the duration of exposure in
the animal study and the anticipated duration of exposure in the human scenario. One aspect of this
adjustment is described above: adjusting for the difference in daily exposure duration. Assuming a
maximum daily exposure duration of 8 hrs when simulant training is performed, the adjusted NOAEL
is 30 * 6/8 = 22.5 ppm. However, the actual anticipated human exposures are brief and infrequent,
associated with special training exercises which are not expected to be a common occurrence.
Therefore the 3-week and 13-week rodent studies represent much more prolonged exposures than the
human exposure scenario, with less opportunity for recovery between exposures. It is common
practice to apply factors of up to 10 to extrapolate from short-term to longer-term toxicity (Dourson
and Stara, 1983). 'in this case, the extrapolation is in the other direction, from relatively long-term to
shorter-term, so an inverse factor is justified. To be conservative, a factor of 1/3 was selected,
yielding an adjusted NOAEL of 22.5 / 0.33 = 67.5. The traditional guideline then applies a safety,
or uncertainty, factor of 100, with one factor of 10 to account for uncertainty in die extrapolation
from animal to man and a second factor of 10 to account for human variability, resulting in the
guideline of 0.7 ppm.
The selection of 100 as the safety factor to be applied in this case follows a convention which,
although basically empirical, can be at least partially justified on the basis of quantitative
pharmacokinetic principles (Dourson and Stara, 1983). For example, the factor often usually applied
-------
for extrapolation from animals to man reflects a conventional wisdom based primarily on experience
with the more common exposure routes, oral and intravenous, for chemicals which are themselves
toxic and are cleared or detoxified by processes which scale roughly with surface area.rather than
body weight. Under these conditions, pharmacokinetic and empirical allometric considerations justify
such a factor for rodent-to-human extrapolation based on the relationship between applied dose and
tissue exposure (area under the concentration-time curve) as a function of body weight (NRC, 1986).
However, for inhalation exposure to a volatile, poorly soluble chemical such as CPFB, these same
principles lead to an expectation of similar area under the curve (AUC) of both parent and metabolites
for equivalent external concentration and exposure duration (that is, for equal time-weighted average
concentrations). In order to make quantitative use of these pharmacokinetic principles, the model
described in this paper was used to calculate the daily AUC in the liver for exposure of rats and
humans to 30 ppm CPFB for 6 hrs. The AUC in the liver predicted for rats was 46 mg/L-hrs,
whereas for humans, even under conditions of moderate exercise, it was 72 mg/L-hrs, a difference of
less than a factor of two. Thus the usual animal to human extrapolation factor of 10 is not justified in
this instance.
The second factor of 10, which accounts for human heterogeneity, would similarly be
susceptible to quantitative evaluation if the distribution of human susceptibilities could be estimated.
For a specific chemical toxicity, the variance of the response distribution in the human population will
depend on the steepness of the chemical dose/response curve, which can be determined from animal
studies, and on the extent of variability in the human population of the pharmacokinetic and
pharmacodynamic parameters mediating the response (Hattis, et al., 1987). Coupling of Monte Carlo
analysis and PBPK modeling provides a method for directly estimating the impact of parameter
variation on risk (Clewell, 1993).
A pharmacokinetically-driven guideline calculation is based on calculation of equivalent
effective tissue doses for the animal and human scenarios. In the case of the liver toxicity associated
with prolonged exposure, the AUC for CPFB in the liver was selected as the appropriate tissue dose.
The AUC is generally regarded as an appropriate dose surrogate for cumulative, reversible toxicity,
such as that seen with CPFB. The daily AUC in the liver calculated by the model at the rodent
subchronic NOAEL of 30 ppm was 46 mg/L-hrs. The selection of an appropriate uncertainty/safety
factor for the pharmacokinetic approach was based on three considerations. First, it was determined
that there was not yet sufficient information on the variability of human susceptibility to liver toxicity
to permit calculation of a more accurate substitute for the default uncertainty factor of 10, so the
10
-------
default value was used. Second, based on Monte Carlo analysis of the impact of parameter
uncertainty on the dose surrogate predictions of the CPFB model, an uncertainty factor of 2.3 was
included for animal-to-human extrapolation. This factor represents uncertainty in the accuracy of the
PBPK model predictions, as distinguished from the default factor of 10, which represents total
uncertainty in the animal to human extrapolation when pharmacokinetics is not considered. The
major contributor to the PBPK uncertainty factor is the lack of data on the human metabolic capability
for CPFB. Additional data, e.g. from in vitro metabolism studies on human liver tissue, would
greatly reduce this uncertainty factor.
The final consideration for the overall uncertainty factor was the infrequent nature of the
anticipated human exposure, as discussed for the traditional guideline. A factor of 1/3 was again
used. Thus the overall safety factor for the pharmacokinetic guideline is 2.3 * 10 * 1/3 - 7.7. The
model was therefore exercised to predict the exposure concentration at which the AUC in the liver for
a human would be one-eighth of the value in the animal at the NOAEL. For an 8-hr time-weighted
average exposure at 1.8 ppm, the calculated AUC in the liver was 5.6 mg/L-hrs, a factor of 8 below
that at the animal subchronic NOAEL. The pharmacokinetically based guideline of 1.8 ppm is more
than a factor of two higher than the traditionally derived guideline.
In addition to liver toxicity, there is limited evidence (Gage, 1970) of behavioral effects at
higher CPFB concentrations (500 to 1000 ppm). Any behavioral deficit induced by CPFB could not
only degrade performance during a training exercise, but could also increase the likelihood of
subsequent exposure through improper use of protective gear. To avoid any behavioral effects
potentially associated with brief exposure to higher concentrations, a short-term guideline was also
developed. The traditional guideline is a 3 ppm ceiling limit based on the 300 ppm NOAEL for
behavioral effects (Kinkead et al., 1991), with a safety factor of 100. The rationale for this guideline
is the same as for the liver toxicity except that no exposure duration adjustment is required since this
is a ceiling limit. In the case of acute behavioral effects, toxicity generally appears to be correlated
with peak concentration rather than area under the curve. The model predicts peak blood
concentrations of 24.6 mg/L and 26.6 mg/L in rats and mice, respectively, at the acute NOAEL of
300 ppm. Providing a margin of safety of 1.8 for PBPK uncertainty and 10 for interindividual
variability, the model determined that an exposure of 31 ppm CPFB would produce a peak blood
level of 1.4 mg/L in humans. This pharmacokinetically derived ceiling is roughly a factor of ten
higher than the traditionally derived value.
11
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Finally, fetotoxic effects were observed in rats dosed orally, with a NOAEL of 300
mg/kg/day (Cooper and Jarnot, 1992). The traditional guideline calculation requires a dose-route
adjustment from the oral route used in the animal study to the inhalation route of concern for human
exposure. The default calculation equates routes on a mg/kg basis, assuming an inhalation rate of 10
cu.m per 8 hrs:
300 mg/kg * 70 kg / 10 cu.m. = 2100 mg/cu.m
2100 mg/cu.m. * 24.45 cu.m./mole / 202.5 g/mole = 254 ppm
Using safety factors of 10 for animal-to-human extrapolation uncertainty, 10 for oral to inhalation
extrapolation uncertainty, and 10 for human variability results in a guideline of 0.25 ppm.
In the pharmacokinetic approach, the oral exposure can be used to develop an inhalation
guideline by using the PBPK model to estimate the peak blood concentrations and AUC for CPFB in
the oral rodent study and comparing them with those achieved during human inhalation exposures.
Both peak concentration and AUC are evaluated as dose surrogates, since the mechanism of
fetotoxicity in this case is not established. For an oral dose of 300 mg/kg in the rat, the model
estimates a peak blood level of 123 mg/L and an area under the blood curve of 109.2 mg/L-hrs. In
this case, PBPK uncertainty factors of 4.8 and 5.1, respectively, are required, due to additional
uncertainty from the oral uptake parameters. Taken together with a factor of 10 for human
variability, the target dose surrogates in the human are a peak blood level of 2.56 mg/L and an AUC
of 2.14 mg/L-hrs. For human1 inhalation, the peak blood level is predicted to be 2.5 mg/L at 56
6
ppm, , while the area under the blood curve for an 8 hr exposure is 2.04 mg/L-hrs at 5.2 ppm. Thus
the pharmacokinetically-derived inhalation guideline for the prevention of fetotoxic effects is 5 ppm,
based on AUC in the blood. This guideline is a factor of 20 above the traditionally-derived guideline.
CONCLUSION
CPFB possesses a remarkable combination of properties, making it an attractive candidate for
use as an intake simulant in chemical defense field training exercises. It is volatile and unreactive,
simplifying dissemination, and mimics the performance of typical vapor threats in terms of persistence
and canister penetration. It does not appear that CPFB would present any significant health hazards
to personnel under the envisioned use. A thorough lexicological evaluation indicates that CPFB is not
acutely toxic or teratogenic and is not likely to be carcinogenic. Chronic liver toxicity was observed
only after prolonged exposure to high concentrations. Based on a pharmacokinetic analysis, it is
12
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recommended that field exercises be designed to avoid short-term exposures to concentrations greater
than 30 ppm, with the daily (8-hour) time-weighted average not to exceed 2 ppm. Since field
analytical methods can measure CPFB at part per billion levels, this should not be an impediment to
its use in training exercises. By comparison, a traditional approach to guideline generation would
result in a short-term limit of 3 ppm with an 8-hr time-weighted average of 0.25 ppm.
REFERENCES
Barnes, D.G., and M.L. Dourson (1988) "Reference dose *icnl. Pharmacol.. 3, p. 234-228.
Gage, J.C. (1970) "The subacute inhalation toxicity of 109 industrial chemicals," Brit. J. Induste, .
Med.. 27, p. 1-18.
Gargas, M.L., Clewell, HJ., and M.E. Andersen (1986) "Metabolism of dihalomethanes in vivo:
Differentiation of kinetic constants for two independent pathways," lojdc^ApjzLJ^rma^ 82, p.
211-223.
13
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Hattis, D., Erdreich, L., and M. Ballew (1987) "Human variability in susceptibility to toxic
chemicals - a preliminary analysis of pharmacokinetic data from normal volunteers." Risk Analysis.
7:4, p. 415-426.
Jepson, G.W., H.J. Clewell, III, and ME. Andersen (1985) "A rapid, physiologically based method
for evaluating candidate chemical warfare agent uptake simulants," AAMRL-TR-85-045, Armstrong
Aerospace Medical Research Laboratory, Wright-Patterson Air Force Base, Ohio.
Kinkead, E.R., W.J. Bashe, D.M. Brown, and S.S. Henry (1987) "Evaluation of the inhalation
toxicity and sensitization potential of chloropentafluorobenzene," in 1986 Toxic Hazards Research
Unit Annual Report, AAMRL-TR-87-020, NMRI-87-2, Armstrong Aerospace Medical Research
Laboratory, Wright-Patterson Air Force Base, Ohio, p. 131-135.
Kinkead, E.R., B.T. Culpepper, H.G. Wall, R.S. Kutzman, C.D. Flemming, C.J. Hixon, and R.R.
Tice (1989) "Evaluation of the potential of inhaled chloropentafluorobenzene to induce toxicity in
F-344 rats and B6C3F1 mice and sister chromatid exchanges and micronuclei formation in B6C3F1
mice," AAMRL- TR-89-037, Armstrong Aerospace Medical Research Laboratory, Wright-Patterson
Air Force Base, Ohio.
Kinkead, E.R., H.G. Wall, C.J. Hixson, R.R. Tice, R.S. Kutzman, and A. Vinegar (1990a)
"Chloropentafluorobenzene: short-term inhalation toxicity, genotoxicity and physiologically-based
pharmacokinetic model development," Toxicol. Indust. Health. 6, 6, p. 533-550.
Kinkead, E.R., S.K. Bunger, E.G. Kimmel, C.D. Flemming, H.G. Wall, and J.H. Grabau (1990b)
"Effects of a 13-week chloropentafluorobenzene inhalation exposure of Fischer 344 rats and B6C3F1
mice," AAMRL-TR-90-064, Armstrong Aerospace Medical Research Laboratory, Wright-Patterson.
Air Force Base, Ohio.
Kinkead, E.R., S.K. Bunger, E.G. Kimmel, C.D. Flemming, H.G. Wall, and J.H. Grabau (1991)
"Effects of a 13-week chloropentafluorobenzene inhalation exposure of Fischer 344 rats and B6C3F1
mice," Toxicol. Indust. Health. 7, 4, p.309-318.
14
-------
Kutzman, R.S., B.C. Myhr, T.E. Lawlor, D.C. Valentine, R.R. Young, H. Murli, M.A. Cifone, and
B M Jarnot (1990) "Genetic toxicity assessment of chloropentafluorobenzene," AAMRL-TR-90-048,
Armstrong Aerospace Medical Research Laboratory, Wright-Patterson Air Force Base, Ohio.
NIOSH (1992) *»pton, nf Toxic Eff^ nf rhamlcal Substances, U.S. Department of Health and
Human Services, Washington D.C.
National Research Counci, (1986) MpUn, W^ «* H;alft. Vo,. 6, d, 6, p. 193-200, NaUona,
Academy Press, Washington D.C.
Ramsey J.C. and M.E. Andersen (1984) "A physiologically based description of the inhalation
pharmacokinetics of styrene in humans and rats," Tojdc^^El^harmacoL, 73, p. 159-175. .
Renner, G. (1988) "Hexachlorobenzene and its metabolism," Tovirol Fnviron. Chem., 18, 1,
p.51-78.
Steele, V. (1987) "Biological activity of chloropentafluorobenzene," AAMRL-TR-87-039, Armstrong
Aerospace Medical Research Laboratory, Wright-Patterson Air Force Base, Ohio.
Tu A M G Broome and A. Sivak (1986) "Evaluation of chloropentafluorobenzene in a battery of
in litre short term assays," AAMRL-TR-86-003, Armstrong Aerospace Medical Research Laboratory,
Wright-Patterson Air Force Base, Ohio.
Vinegar A D.W. Winsett, M.E. Andersen, and R.B. Conolly (1990) "Use of a physiologically
based pharmacokinetic model and computer simulation for retrospective assessment of exposure to
volatile toxicants," Tnhal. ToxicoU. 2, p. 119-128.
15
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Table 1: PBPK Model Parameters
UNSCALED PARAMETERS
BW
KA
ALVS
DS
QCC
QPC
Body Weight (kg)
Oral Uptake Rate (/hr)
Alveolar Dead Space (Fraction)
Bronchiolar Dead Space (Fraction)
Cardiac Output (L/hr, 1 kg animal)
Alveolar Ventilation (L/hr, 1 kg
Mouse
0.023
5.0
0.0
0.3
16.5
29.0
Rat
0.22
5.0
0.0
0.3
11.6
21.2
Monkey
8.7
5.0
0.4
0.45
12.0
17.0
Human
70.0
5.0
0.0
0.3
18.0
35.0
Tissue Blood Flows (Fraction of Cardiac Output): ,
QFC
QGC
QLC
QMC
QRC
QSC
Flow to Fat
Flow to GI Tract
Flow to Liver
Flow to Bone Marrow
Flow to Rapidly Perfused Tissues
Flow to Slowly Perfused Tissues
0.030
0.166
0.036
0.110
0.409
0.249
0.058
0.183
0.032
0.110
0.362
0.255
0.052
0.185
0.065
0.110
0.348
0.240
0.052
0.185
0.065
0.110
0.348
0.240
Tissue Volumes (Fraction of Body Weight):
VBL
FVC
VGC
VLC
VMC
VRC
VSC
Volume of Blood
Volume of .Fat
Volume of GI Tract
Volume of Liver
Volume of Bone Marrow
Volume of Rapidly Perfused Tissues
Volume of Slowly Perfused Tissues
0.070
0.100
0.033
0.050
0.030
0.041
0.550
0.070
0.070
0.033
0.040
0.030
0.020
0.600
0.070
0.190
0.045
0.027
0.020
0.026
0.709
0.070
0.050
0.045
0.027
0.020
0.026
0.569
16
-------
Table 1 (continued): PBPK Model Parameters
Partition Coefficients:
PB
PF
PG
GI Tract/Blood
Bone Marrow/Blood
Richly Perfused Tissue/Blood
Slowly Perfused Tissue/Blood
=3===
Metabolic Parameters:
KFC
Rate Constant for 1st Order Pathway
(/hr - 1 kg animal)
Affinity of Saturable Pathway (mg/L)
Maximum Velocity of Saturable
Pathway (mg/hr, 1 kg animal)
17
-------
Table 1 (continued): PBPK Model Parameters
SCALED PARAMETERS
QC = QCC*BW**0.75
QP = QPC*BW**0.75
QF = QFC*QC
QG = QGC*QC
QL = QLC*QC
QM = QMC*QC
QR = QRC*QC
QS = QSC*QC
VBL = VBLC * BW
VF = VFC*BW
VG = VGC*BW
VL = VLC*BW
VM = VMC*BW
VR = VRC*BW
VS = VSC*BW
KF = KFC/BW**.25
VMAX = VMAXC*BW**0.75
DOSE SURROGATES
Amet Total amount metabolized per unit body weight (mg/kg)
AUCB Area under the curve of arterial blood concentration of CPFB (mg/L-hrs)
AUCL Area under the curve of liver concentration of CPFB (mg/L-hrs)
AUCM Area under the curve of CPFB in the bone marrow (mg/L hrs)
CA Concentration of CPFB in the arterial blood (mg/L)
CL Concentration of CPFB in the liver (mg./L)
CM , Concentration of CPFB in the bone marrow (mg/L)
CV Mixed venous blood concentration of CPFB (mg/L)
Dose Total amount inhaled during exposure (mg/kg)
= integral of QP * (CALV - CX) / BW
18
-------
Table 2: Summary of In Vitro Results for CPFB
IN VITRO ASSAY
Ames Salmonella mutagenicity:
- S9 activation
1 + S9 activation
1 CHO/HGPRT gene mutation:
- S9 activation
1 + S9 activation
1
1 CHO sister chromatid exchange:
—
- S9 activation
| + S9 activation
1 CHO chromosome aberration:
1 - S9 activation
1 + S9 activation
L — —
1 Primarv rat hepatocyte DNA repair
|j BALB/C-3T3 cell transformation:
|| - S9 activation
II + S9 activation
7
Tu et al.
-
-
-
-
-
+/-
+ /-
+ /-
-
1 a
J _
1"
Steele
-
-
-
-
+/-
+ /-
—
+ /-
'"—
+ /-
-
1 + /-
1 +
Kutzman et al.
-
-
-
-
-
+
-
-
-
~
Not reported.
19
-------
Figure 1
Figure 2
Figure 3
Figure 4
FIGURE CAPTIONS
Diagram of the physiologically based pharmacokinetic model of CPFB.
Computer simulation (solid line) vs. observed (x) chamber concentrations (ppm) over
time (hours) for rats exposed to initial concentrations of 10, 250, 1000, and 1800
ppm CPFB in a closed, recirculating chamber system. (Reproduced from reference 4)
Model-predicted (lines) and measured (boxes) venous blood concentrations on 11th
day of exposure of rats to CPFB for 6 hours per day at 30 ppm (a), 100 ppm (b), and
300 ppm (c).
Model predicted (lines) and measured (points) exhaled air concentrations for rhesus
monkeys exposed to 300 ppm CPFB for 17 - 20 minutes.
20
-------
QP'CI
QOCV
LUNG
GAS
EXCHANGE
QR-CVR
QM*CVM
RAPIDLY
PERFUSED
TISSUES
MARROW
QS*CVS
I QF'CVF
SLOWLY
PERFUSED
TISSUES
FAT
Gl TRACT
QC'CA
QR*CA
QM-CA
QS*CA
QF*CA
!QG*CA
QG'CVG
(QL+QG)«CVL
LIVER
_|O=OR!NK
1 QL-CA
MFO ty ty GST
Figure 1
-------
Wdd U3SWVHO 038010 Nl ONOO) - dO
Figure 2
_
-------
4-
.3-
°0 10 20-30 40. 50
10
3
i
3
S
i
a
10 20 T 30 40 SO
10-
8-
s"
4-
2-
c
a ' : :
X9 ' ! i !
7 ; '
m-
~^^" i
10 20 T 30 40 SC
Figure 3
-------
1
g
rs
S 8 g
IW«O«o *~
S 8
HMAO-o "
§
Figure k
-------
BIOGRAPHICAL SKETCH: DR. MICHAEL L. DOURSON
located in Cincinnati, Ohio. ECAO is a riSK-°~'° ntfict> of Health
the D.S. Environmental Protection agency's (EPA-s) _office "^J^
and Environmental assessment. Dr. to^s°* >£* J^1* ^^ °f
i!vrdfffPerSrorXEs?'-°ohatrli%^rofSl^^^^
1 9Sriea^ip ?
tjr-s^^SiS ^ss
?r a pSst pSSident of the Society of Toxicology's Specialty
Sl^ioS £1 ^k Assessment. Dr Dourson ff.^^^^3
publications and presentations in the area of risk assessment,
primarily for the assessment of noncancer health effects.
-------
RISK ABOVE THE REFERENCE DOSE (RfDVBENCHMARK DOSE (BMD)
Michael Dourson and Richard Hertzberg
Abstract
Current methods to estimate noncancer health risk are limited. Situations exist
where subthreshold doses such as Reference Doses (RfDs) or Reference Concentrations
(RfCs) are exceeded and little is known about the possible health risk. A recent model
indicates that toxicity data viewed as categories of pathology has potential for exploring
such risk. What appear to be reasonable estimates of rjsk above the RfC are found with
toxicity data for manganese.
This model is also compared to another new approach - the benchmark dose for
several chemicals. Differences between these two approaches are briefly discussed.
Introduction
The RfD has been the mainstay of noncancer risk assessment in the U.S. EPA for
several years. The RfD is defined as: an estimate (with uncertainty spanning perhaps an
order of magnitude) of a daily exposure to the human population (including sensitive
subgroups) that is likely to be without an appreciable risk of deleterious effects during a
lifetime.
As displayed in Figure 1, much interest exists in the estimation of health risk above
some level, such as an RfD or RfC. Little progress has been made, however, primarily
due to the multiplicity of effects, the changing severity and intensity of individual effects
as dose increases, and the lack of mathematical tools.
-1-
_
-------
The purpose of this manuscript is to present and analyze toxicity data for several
chemicals with a method of Hertzberg (1989) that addressees some of these issues.
Risks above the RfD or RfC are estimated with this model.
In addition, recent interest has been expressed in replacing the NOAEL-based RfD
with a benchmark dose (BMD): a statistically-derived lower confidence limit on a dose
associated with a specified level of excess risk such as 1, 5 or 10%. The arguments
favoring the BMD are that statistical models can be used, and that a consistent
interpretation can be made of the BMD across studies and across chemicals. We show
briefly in this paper (and more extensively elsewhere: Hertzberg and Dourson, 1993) that
the BMD is not consistent with the philosophy of the RfD, and that it leaves several issues
unresolved.
Methods
We reviewed inhalation toxicity data for manganese and judged exposure or dose
groups as one of four very broad categories of toxicity; either no-observed effect, no-
observed-adverse effect, adverse effect, or frank effect. We regressed these ordered
categories against both concentration (or dose) and exposure duration using a logit
model. Ordered regression obviates pathologic "distances" among categories.
Based on an analysis of all data as shown schematically in Figure 2 for one study,
it is possible to determine the probability of NOEL, NOAEL, LOAEL and PEL for a given
chemical. This is shown hypothetical^ in Figure 3. In mathematical terms, categorical
regression can be seen as follows:
-2-
-------
RfD Definition
"is likely to be"
"without appreciable risk"
"deleterious effect"
Regression Model
P(*)>0.95
r<10'2
toxicity category = moderate or
lethal adverse effect
This leads to a new RfD definition: P(r<10'2|dose 0.95, where r = P
(severity>1). We selected 10'2 risk and the 95%^confidence level only to illustrate the
method. Standard values for these decision criteria have not been adopted by EPA. The
value 10~2 is a more realistic goal than the 10'6 risk often used for carcinogenic risk
because most of the noncancer effects we consider are sublethal, and many are
reversible.
Results
Toxicity data for manganese were excerpted from available literature. These were
available incidence data from human studies as shown in Table 1. The resulting
probability statements for manganese are interpretable as human incidence for either an
adverse effect (e.g., finger tremor) or frank effect (e.g., disturbed gait) as shown in
Figure 4. For most published toxicity studies, effects are noted only for the dose group,
so the probability statements are likewise interpretable only at the dose group level (i.e.,
the probability that a dose group will have the effect).
Discussion
The categorical regression model described by Hertzberg and Miller (1985) and later
papers (Hertzberg, 1988, 1992; Hertzberg and Wymer, 1991; Guth et al., 1991; Farland
-3-
-------
and Dourson, 1991) is one approach that incorporates judgments of toxicity along with
response rate into a statistical characterization of the overall exposure-response
relationship. The model can be used to estimate a BMD, or can be used to estimate
toxicity risk at any exposure level. The "risk" is no longer a single number, but a vector
of numbers, one for each category.
The categorical regression approach has two distinct advantages over the NOAEL-
RfD and BMD-RfD procedures described above. First, the approach is easily adapted
to most types of toxicity data, from judgments of overall severity of toxic effect for the
dose group to measured responses on each individual. Second, all relevant toxicity data
are included in the regression. Third, a consequence of the second advantage, this goal
of this approach is highly consistent with that of the NOAEL-RfD method: to produce
regulatory information that incorporates all toxic effects. In particular, an exposure level
can be estimated by regression that is quite similar in interpretation to the NOAEL-based
RfD. Fourth, the judgmental step involves evaluation of overall toxic impact on the
exposed individual, allowing comparison across target organs, and across chemicals
when several organs are affected.
However, the probabilities generated by categorical regression are usually limited
to whether or not a dose or exposure group, and not an individual, is at risk. When
incidence data are used in the analysis (such as for manganese shown here), actual
population risk estimates are possible.
Perhaps the greatest advantage of categorical regression is that this method can
compare the likely health risk above the RfD or RfC for several chemicals. In risk
management decisions, such comparison is often necessary. Figure 5 demonstrates this
concept hypothetical^.
-4-
-------
Disclaimer
Although the research (or other work) described in this article has been funded
wholly or in part by the United States Environmental Protection Agency, it has not been
subjected to the Agency's required peer and administrative review and, therefore, does
not necessarily reflect the view of the Agency. No official endorsement should be
inferred.
References
Farland, W. and M.L Dourson. 1992. Noncancer health endpoints: Approaches to
quantitative risk assessment. (In press)
Guth, D.J., A.M. Jarabek, L Wymer and R.C. Hetzberg. 1991. Evaluation of risk
assessment methods for short-term inhalation exposure. Presentation at the 84th Annual
Meeting of the Air & Waste Management Association, June 16-21, 1991, Vancouver,
British Columbia.
Hertzberg, R.C. 1988. Studies on toxicity applicable to risk assessment (STARA) user's
guide. Quantitative toxicity data and graphics on environmental chemicals.
Environmental Criteria and Assessment Office, U.S. EPA, Cincinnati, OH. February.
Hertzberg, R.C. 1989. Fitting a model to categorical response data with application to
special extrapolation to toxicity. Health Phys. 57(Suppl. 1): 404-409.
-5-
-------
Hertzberg, R.C. 1992. Studies on toxicity of mixtures and interacting chemicals user's
guide. (MIXTOX) U.S. EPA, Environmental Criteria and Assessment Office, Cincinnati,
OH 45268. Available on diskette.
Hertzberg, R.C. and M.L. Dourson. 1993. Using categorical regression instead of a
NOAEL to characterize a toxicologist's judgment in noncancer risk assessment.
Submitted.
Hertzberg, R.C. and M. Miller. 1985. A statistical model for species extrapolation using
categorical response data. Toxicol. Ind. Health. 1: 43-57.
Hertzberg, R.C. and L. Wymer. 1991. Modeling the severity of toxic effects. In:
Proceedings papers from the 84th Annual Meeting and Exhibition of the Air & Waste
Management Association, June 16-21, 1991, Vancouver, British Columbia.
-6-
-------
FOG OF
UNCERTAINTY
o
-------
SLIGHT BODY WEIGHT DECREASE
RfD
I -
NOEL NOAEL LOAEL
UFxMF
FAT IN LIVER CELLS
(CRITICAL EFFECT)
CONVULSIONS
ENZYME CHANGE
PEL
FIGURE 2
Typical judgments on doses used to determine RfDs in on© study.
-8-
-------
For any chemical it is possible to....
NOEL
DOSE
NOAEL
DOSE
AEL
DOSE
FEL
DOSE
FIGURE 3
Probabilities of various effect or no effect levels, with dose based on a review of
all data.
-9-
-------
CL
LLJ
a:
o
1
0)
o
o
o
O)
-a
1
3
IS
TJ
en
3-
t3
0)
o
E
Q)
0}
CD
O)
CD
8
1
JK
o
1
O CD
P
li
a. &
O
o
-10-
-------
CHEMICALS
ABC
RfDs
DOSE
1-FOLD 1OO-FOLD
MULTIPLE OF THE RFD
FIGURE 5
For multiple chemicals it is thus possible to compare the risk of unacceptable effect
at existing exposures above the RfD or RfC.
-11-
-------
TABLE 1
Human Studies on Manganese Exposure and Resulting Toxicity
S3====— — — r-
Study
.
Bradawy and
Shakour, 1985
Chandra et al.,
1981
Cook et al.,
1974
Davies, 1946
Emara et al.,
1971
Flinn et al.,
1941
Lauwery et al.,
1985
Nogawa et al.,
1973
Rodier, 1955
Roels et al.,
1987
Saric et al.,
1977
•
Number
=====
30
35
30
20
20
20
6
40-124
36
34
85
1222
-4000
141
204
190
66
268
17
•18
1
Exposure (HEC)
(mg/m3) I
I
0.36
1.1
2.5
0.11
0.20
0.63
—
0.89-4.0
0.072-4.8
2.4-15
11-32
0.34
0.004
91-160
0.35
0.00005-0.00007
0.0007-0.01 1
0.17-0.38
0.11-1.8
3.4-4.0
5.9-7.3
1.8-5.8
Duration
(days)
=L.
?
?
?
7370
7670
5150
365-6205
2920
365-5840
365-1095
2920
730
30-3650
2592
9
?
-4015
-4015
-4015
-4015
>7300
Effect
=—4
CMS
CMS
CMS
CMS and
pulmonary
CMS and
pulmonary
CNS and
pulmonary
CNS
pulmonary
CNS
CNS
reproductive
pulmonary
CNS
CNS and
pulmonary
none
CNS
none
CNS
CNS
CNS
CNS
Severity and
- Percentage
AEL, ?
AEL, ?
AEL, ?
AEL, 25 I
AEL, 50
AEL, 40
FEL, ?
PEL, 3 1
FEL, 17
FEL, 24
J
AEL, ?
AEL, ? !
FEL, 4
AEL, 15
NOEL, 100
AEL, 5.8
NOEL, 100
AEL, 20
AEL, 18
AEL, 28
FEL, 100
-12-
-------
Study
Schuler
et al., 1957
Smyth et al.,
1973
Tanaka and
Lieben, 1969
=====
Number
83
71
38
117
TABLE 1 (cont.)
Exposure (NEC)
(mg/m3)
1.8
4.8
0.00004ato<1.8
>1.8
Duration
(days)
2980
2920-9490
?
7
Effect
CNS
CNS
none .
CNS
•
.
Severity and
- Percentage
PEL, 1 1
PEL, 7
NOEL, 100
PEL, 6
aAmbient background
-13-
-------
BIOGRAPHICAL SKETCH: DALLAS M. HYDE, Ph.D.
Dr. Hyde received his undergraduate degree from the University of California Irvine
in 1 967; his Master's from Whittier College in 1 972; and his Ph.D. from the University
of California, Davis in 1976.
to the University of California, Davis in 1979.
In 1985-1986, Dr. Hyde was the recipient of the National Research Service Award
Senior Fellowship. NIH, in the laboratory of Dr. Peter Hensen, at the National Jewish
Hospital in Denver, CO.
Dr Hyde's research focus has included the role of neutrophils in oxidant-induced
e^thelia? Injury and repair, and cellular and molecular mechamsms of f.broblast
proliferation and collagen synthesis in experimental pulmonary fibrosis.
I/-* •'
-------
Morphometry of Pulmonary Toxicity in Mammals:
Implications for Risk Assessment
Dallas M. Hyde, Robert P. Bolender*, Jack R. Harkemab and Charles G. Plopper
Department of Veterinary Anatomy and Cell Biology, School of Veterinary Medicine, University
of California, Davis, CA; Department of Biological Structure,3- School of Medicine, University of
Washington, Seattle, WA; Inhalation Toxicology Research Institiute,^ Albuquerque, NM.
Abbreviated title: Morphometry and Risk Assessment
Address correspondence to: Dr. Dallas M.. Hyde
Department of Veterinary Anatomy and Cell Biology
School of Veterinary Medicine
University of California
Davis, CA 95616
-------
ABSTRACT
Recent advances in quantitative morphology provide all the tools necessary to obtain structural
information in the lung that can be quantified and interpreted in the three dimensional world of
toxicology. Structural hierarchies of conducting airways and parenchyma of the lung provide 1)
numbers of cells per airway, lobe, or lung, 2) surface areas of cells, airways, and alveoli, 3)
length of airways and vessels and 4) volumes of cells, alveoli, airways, vessels and individual
lobes or the entire lung. Unbiased sampling of these subcompartments of the lung requires
fractionation of lobes or individual airways. Individual airways of proximal and distal generations
are obtained by airway microdissection along one axial pathway and comparisons made between
airway generations. Vertical sections of selected airways are used to sample epithelium and
interstitium. Using this unbiased approach of quantitative morphology, we have shown that
inhalation of low ambient concentrations of ozone (0.15 ppm) near or.at the United States National
Ambient Air Quality Standard (NAAQS) (0.12 ppm ozone) induces significant alterations in
bronchiolar epithelium and interstitium in nonhuman primates but not rats. The alterations do not
appear to be concentrations time-dependent, thereby bringing into question the current NAAQS
that may be at or above the threshold for distal airway injury in primates.
Keywords: Morphometry, risk assessment, lung, design-based methods, stereology
2 '
-------
INTRODUCTION
An unbiased morphometric assessment of pulmonary toxicity in animal lungs must consider
the complexity and diversity of the entire branching airways and parenchyma. The extrapolation to
humans of studies of toxic agents injurious to the respiratory system using animal models assumes
9
comparability in the structure and function of the respiratory system of these model species and
humans. The underlying assumption is that data, especially morphometric data of lung structure,
obtained in model species can be extrapolated to humans.
Ozone is the most reactive and toxic oxidant gas in photochemical air pollution. Ozone is also
the principal air pollutant in many urban areas during the summer months. In recent years,
maximal monthly concentrations of ozone ranging from 0.1 to near 0.3 parts per million (ppm)
have been reported in Mexico City. 1'2 Ozone inhalation produces injury in at least three target
areas of the respiratory system of animals: nasal cavity, trachea and the centriacinar region.3-13
Recent reports that some pulmonary function impairment was induced in exercising children and
adult humans exposed to ozone concentrations at or near the ambient concentration in the current
U. S. National Ambient Air Quality Standard (NAAQS) for ozone (0.12 ppm) have opened to
question the health safety of the NAAQS.14'21
The majority of studies defining the pathogenesis of ozone-induced injury have been
conducted using the laboratory rodents. Small laboratory mammals show an initial response of
cellular necrosis, exfoliation, degranulation of secretory cells, followed by epithelial hyperplasia
and hypertrophy.6.8,9,11,22,23 Experimental studies with rats at-concentrations near the
NAAQS, suggest that the lungs of rats are relatively insensitive to these environmentally relevant
concentrations of ozoneA24 Direct assessment of the human susceptibility to injury by ozone
inhalation is limited to physiologic assessment or measurement of bronchoalveolar lavage cells and
fluid following short-term, low level exposures; thus, accurate assessment of the risk of ambient
ozone to humans is difficult.25,26
Extrapolation from data collected using the laboratory rat suggests that there is minimal risk
for humans at ambient concentrations of ozone. In contrast, monkeys exposed to 0.15 ppm ozone
-------
for 6 or 90 days, 8 hours per day, had significant nasal epithelial lesions of ciliated cell necrosis,
attenuated cilia, and secretory cell hyperplasia and bronchiolar epithelial lesions of hyperplasia and
hypertrophy of nonciliated bronchiolar epithelial cells and intraluminal accumulations of
macrophages.10,27 The bronchiolar lesion was also characterized by significant thickening of the
interstitium 27 These comparative quantitative results of differential sensitivity to ozone inhalation
of the nonhuman primate versus the rodent wer& only possible because of the use of carefully
applied morphometric methods. This paper reviews the state-of-the-art application of
morphometric methods to pulmonary tissue evaluated in the assessment of ozone-induced lung
injury.
'METHODS
We will use three guiding principles in morphometry: 1) Use design-based methods to
quantify structural changes, 2) Use structural hierarchies to link and interpret experimental data,
-------
HIERARCHICAL DATA: The lung is a complex organ composed of numerous compartments
ranging in size from molecules to tissues. Hierarchies allow us to organize data according to the
size of the structures. Hierarchy equations define the relationships among and link data within
and across hierarchical levels.28 For example, if we desire the number of type I epithelial cells in
the lung, we only need to multiply the number of type I epithelial cells per volume of interalveolar
septal tissue by the volume density of the interalveolar septal tissue per parenchymal tissue, the
volume density of parenchymal tissue per lung, and the volume of the lung. In this example the
object at one magnification becomes the reference at the next higher level of magnification. Also,
information is provided at each level or magnification and not just at the organ level. Two general
guidelines emerge from hierarchy organization: 1) Use the lowest reasonable magnification
(acceptable resolution) to increase sample size for measurements and 2) If major compartments
and their subcompartments cannot be measured at the same magnification, then the magnification
should be increased to optimize resolution in the subcompartmenL
CRITICAL DATA: A critical data set is required to detect and interpret quantitative data in any
organ.28 These data include the volume of the structure, the number of cells in the structure, and
the structural components or densities. The critical data set allows one to move data about a
structural hierarchy, detect and interpret structural changes, and create links to other data types.
ABBREVIATIONS, SYMBOLS, AND TERMINOLOGY: Pulmonary structures can be
described as having volumes, surfaces, lengths, and numbers: V, S, L, N. Structural densities
relate these parameters to a unit of reference volume: V/V, S/V, L/V, N/V (represented as Vy, Sy,
LV, Ny). These four defining parameters are further defined by accompanying symbols. The
symbol (i/ref) defines the ratio of two compartments as given for the densities: Vv(i/ref),
Sv(i/ref), Lv(i/ref), NyO/ref). The compartment of interest, the small'"!", is related to the
reference compartment, "ref'. For example, the volume density of capillaries within the lung is
represented as Vy(ca, lu). We use the first two the first two letters of the compartment to
abbreviate the names. For example, lung becomes lu, trachea tr, capillaries ca and collagen co.
Extra letters can be added to avoid duplicates.
-------
TEST GRID SYSTEMS: Point and intersection counting, using coherent test systems are more
efficient for collecting raw data than digitizers requiring hand tracing of objects.31 Hence, for aU
stereologic measurements other than those that can be done by image analysis^ point and
intersection counting using test grid systems prevail. It should be noted that even when image
analysis can be applied to lung tissue, such as quantising the volume of stored mucosubstance per'
surface area of epithelial basal lamina, it is only 12-fold more efficient than manual methods.33
We prefer using the efficient point and intersection counting methods by employing coherent test
systems designed to give reference points (Pr ), reference line lengths (L, ) and reference areas
(Ar) after Weibel,31 CruZ-Orive34 and Baddeley et al,^ The counting rule of Gundersen36
should be followed when making profile counts on any of the grids. The rule is to count all
profiles totally within the counting frame that do not intersect the "forbidden lines" at any point.
The forbidden lines include two adjacent borders of the frame (left and bottom as marked by solid
lines on the grids) and extended lines from left top and lower right corners.
VOLUME OF THE STRUCTURE: One of the most common starting points and our first critical
data is the volume of the lung or its individual lobes. One of the most direct methods is to
systematically cut fixed lung lobes into slabs of equal thickness, dehydrate, embed and section
sampled slabs and determine all data within a volume that is common to all levels of observation
(e.g., a volume that is fixed, dehydrated, embedded and sectioned). By incorporating a common
reference into the experimental study, it is then possible to move data freely across the various
levels of the hierarchy without fear of experimental bias. To estimate the volume of a lung lobe,
take about ten samples from the slabs (selected systematically), determine their cumulative area by
point counting, and multiple by the average slab thickness (Cavalieri method^. The volume of the
individual slabs can be estimated more precisely by defining their shape as a prismatoid using.
computer digitization and analysis, but this level of precision is unwarranted and not a marked
improvement on the Cavalieri method of volume estimation .37
Another approach, the optical volume fractional (OVF), provides estimates for the volume
of the structure, the total number of cells in the structure, and the numerical density of cells.38 It
-------
combines two of the primary tools of stereology, the fractionator39 and the optical "disector".40
The fractionator systematically subdivides a structure into smaller and smaller fractions until a final
fraction is obtained. Volume is estimated in the final fraction by the Cavalieri method and related
by fractions to estimate the volume of the entire structure. If we count the number of cells in the
final fraction using the OVF method, then we can estimate the number of cells per unit volume of
compartment and within the entire structure. The OVF method allows us to build structural
hierarchies for the lung by establishing links between light and electron microscopy. As long as
specimens are treated the same (similarly fixed, dehydrated, and embedded) the links between light
and electron microscopy are valid.
NUMBER OF CELLS IN A STRUCTURE: The second critical data required is the number of
cells in a structure (lung lobe, airway, vessel, alveolus, etc.). The most direct and unbiased
method is to count cells in 3-D space.41.42 This is the basis of the "disector" principle. Counting
methods based on the disector include the fractionator,40 optical fractionator,43 optical' volume
fractionator,38 nucleator,44 and selector.45 The point sampled intercept,46 mean boundary,47
and boundary sampled intercept48 methods use single random sections and count objects in 2-D
space. They are largely unbiased for shape, but not for size.
Using direct counting of cells in 3-D space we are given three options for estimating the
number of ceUs in the structure. If we want to estimate only total cell number, the fractionator or
optical fractionator will be the easiest. Both methods are efficient and independent of shrinkage
and swelling artifacts. For hierarchical studies, wherein data are collected from several levels
within the structure, we will want numerical density estimates for cells (number in the volume of
the various compartments). These estimates become critical for detecting changes in cell
compartments, such as organelles, because they allow us to calculate average cell data from
stereological densities. For example, assume we estimate cell numerical density of type n cells
within the volume of interalveolar septa (Ny(#2,is)) using the optical dissector and the volume
density of type H cells within the volume of interalveolar septa (Vv(ii4s), then we can calculate the
average volume of type II cells (V(ii)) by dividing the volume density by the numerical density as
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follows:
V(ii,#2) = Vy(iUs) / Nv(#2,is) ,
where #2 represents the number of type E cells which in the denominator of V(ii,#2) becomes 1.
The real units for volume are in all in the same cm3 units and the reference volumes for VV and
NV are the same and thereby divide to 1.
OPTICAL DISECTOR: The optical disector method counts cells directly in a measurable
volume 40,49 whether light or laser confocal microscopes are used to optically focus through a
thick section (usually about 20 ^m thick), a short depth of focus (1 to 3 ^m) is essential to
optically section the tissue and a length gauge required to precisely move in the Z direction.
Usually a lens with a high numerical aperture satisfies the short depth of focus problem. This
unbiased counting method is direct provide we use a 2-D unbiased counting frame36 and extend
the counting frame concept by excluding structures counted on either the top or bottom of the cube.
We estimate the reference volume by point counting an optical section in the middle of the cube that
provides us with a reference area that is multiplied by the distance traveled in the Z direction for
counting structures. Note we can use the area of the 2-D counting frame if it is totally filled with
the reference area.
DENSITIES: A density is the ratio of two compartments, a compartment i in the numerator and a
reference compartment, which is usually a volume in cm3, in the denominator. The four standard
densities include volume, surface, length, and number. Since the numerator and denominator in
densities are both variables, they cannot reliably detect changes unless they are related to the
volume of the structure.
Two types of sections can be generated when structures are sampled systematically: (1)
vertical sections (blocks rotated randomly about a vertical axis and sectioned), or (2) isotropic
uniform random (IUR) sections (blocks oriented randomly in all directions and sectioned). Since
the lung contains many aniostropic (oriented) structures, only systematic sampling and vertical
sectioning guarantee unbiased estimates for all four densities. Estimates of surface and length
densities in the lung require a cycloid test grid oriented with respect to the vertical axis. Volume
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and numerical densities can be made with vertical or IUR sections and thus the grid type is not
specific.
VOLUME DENSITY: Volume density, Vy , is independent of the sectioning angle and the
orientation of anisotropic structures because it affects both the object and reference phase equally.
Volume density should be estimated by point counting techniques. Point counting has been shown
to be the most efficient method of estimation^ 1 and it uses the formula
Vy(i,r) = Pi/Pr
where Pj is the number of point "hits" on the compartment of interest and Pr is the number of
point "hits" on the reference compartment.50
SURFACE DENSITY: Surface density, Sy , is influenced by both the sectioning angle and the
shape of anisotropic structures.^ 1 For isotropic structures, surface density can be defined .
sVi,r = 2Ii/ LT
where Ij is the number of intersections of the object surface and Lj. is the test system length of the
reference component.52,53 xMs equation is valid for test lines that are isotropic uniform random
in three dimensional space. To meet this requirement using a lattice grid, the microstructures must
be distributed uniformly and randomly and their orientation must be isotropic. The use of vertical
sections, defined along the plane of preferred orientation for anisotropic microstructures, and a
cycloid test grid system,35 gives surface density estimates that correct for anisotropic orientation
directly using the equation for Sy. Vertical sections alone, however, do not guarantee isotropic
random encounters with the orthogonal test lines used to estimate surface and length densities in
IUR sections. Sin weighted test lines alone the vertical axis, arranged continuously as cycloids
correct this bias of vertical sections.35 The requirements of vertical sections according to
Baddeley et al.35 are as follows: (a) Identify a vertical axis (along a preferred or arbitrary axis),
(b) All vertical sections must be cut parallel to the vertical axis. The test grid must be oriented with
respect to the vertical axis, (c) All vertical sections must have random positions (systematic
sampling of slices) and isotropic random orientation (spin about vertical axis), and (d) A test line
on vertical sections must be weighted proportional to sin 0, where 6 is the angle between the test
-------
line and the vertical direction. Some examples of vertical sections are: (a) longitudinal sections of
skeletal muscle, (b) sections of skin and other flat epithelial (e.g., tracheobronchial epithelium in
microscopic windows) normal to the exterior macroscopic surface, and (c) sections of an arbitrary
organ, obtained by cutting the organ into parallel slabs (with an arbitrary common direction) and
then placing some of the slabs on a flat surface (horizontal plane) and sectioning normal to the flat
surface. To obtain vertical sections of tubular organs (e.g., tracheobronchial airways), the organ
or airway must be opened along its axis, flattened along its abluminal surface that becomes the
horizontal plane. Relative to this defined horizontal plane, the vertical sections must be selected in
a random orientation. For test grid systems, superimposed on vertical sections, a test line is given
a weight proportional to sin 6, where 9 is the angle between the test line and the vertical direction.
Either a numerical weight for each intersection count obtained with test lines at a given angle or a
test system in which test lines at an angle 9 to the vertical have length proportional to sin 9 is
required for a correct weighting factor. The cycloid grid has a unique property where the tangent
part of the curve is at an angle 9 to the vertical axis that has length proportional to sin 9.35 Thus,
the sin 9 weighting factor is incorporated into the grid and with the intersection count per unit
length of cycloid test curve gives an unbiased estimate of surface density.
LENGTH DENSITY: Estimating lengths with IUR sections can become unusually problematic
when linear structures have anisotropic orientations in tissues.54 A new design-based method
avoids this problem of anisotropy by using vertical sections and projected images.54,55 with
vertical sections, all linear structures contained within the volume of a thick section or slice are
- projected onto a plane. Counting the intersections between a cycloid test line and the linear
structure and measure the section thickness with a length gauge provides an unbiased estimate of
LV. To collect data with this method, a cycloid test grid must be oriented with respect to the
vertical axis of the section, not perpendicular to it as with SV estimates. To estimate the length of
capillaries per volume of interalveolar septa (LV(ca,is)) we need to count the total points hitting on
the reference component (is) and the number of intersections I(ca) between test lines and the
capillaries. We then evaluate the Gokhale equation:54
10
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Lv(ca,is) = 21 (t * 21(ca) /1 L(is)),
where t is the mean section thickness and X L(is) the total test line length in the reference
component (is).
NUMERICAL DENSITY: Numerical density, Nv , was introduced previously in the number of
cells in a structure. When NV is desired, one simply uses the optical disector and measures the
volume in which the cells are contained. If the optical director is combined with the fractionator
method, it provides the most powerful method, the optical volume fractionator.38
STATISTICAL CONSIDERATIONS: Statistical considerations relative to stereological
measurements have been presented in detail elsewhere.56-60 However, a compelling argument on
the contribution to the total variance of a group of animals in a stereological study of the harmonic
mean thickness of the glomerular basement membrane was provided by Gundersen and Osterby.^8
They showed that animals contributed 70%, blocks 19%, fields 8% and intercepts and measuring
3% of the total variance. In our laboratory and those of others,61 the results of stereological
measurements of lung tissue agree with those in kidney and identify animal and block variance as
the greatest source of variation in a study. A logical approach to this problem means that we
always use sufficient number of animals and blocks per animal in our studies. Then in turn we use
the minimal number of fields per block and points per field to estimate the block values. Using
ratio estimators, we sum the values over blocks and then use the means of the block values to
estimate the organ or animal value.56
FIXATION: Fixation and sampling are critical aspects of any morphometric study of the lung. If
the pulmonary epithelium is to be examined only by light microscopy, a 10% buffered formalin
solution is adequate. However, the use of electron microscopy usually requires a glutaraldehyde-
paraformaldehyde fixation (440 mOsm, pH 7.4).62 since no true dimensions of cells and their
organelles are known, some investigators have employed quick-freezing and cryosubstitution to
compare morphological features with those in fixed tissues,63 or to examine unfixed antigenic
determinants. The relative osmotic effects of glutaraldehyde and buffer solutions were evaluated
on the. harmonic mean thickness of the blood/air barrier and the shape of erythrocytes in pulmonary
11
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capillaries by instiUing fixative in lungs of rats.64 Mathieu et aL**> showed that 300 mOsm was
required to maintain eiythrocyte shape and that glutaraldehyde concentration exerted an osmotic
effect, even though it was less important than the buffer. In essence, the harmonic mean thickness
of the blood/air barrier varies inversely proportionally to the total osmolarity. Our most commonly
used fixative is 1% glutaraldehyde, 1% paraformaldehyde at pH 7.4 and adjusted to 360 mOsm
with cacodylate buffer. However, the goal of a study also dictates the fixative to be used. If.
preservation of antigenic determinants is critical, then mild fixation may be required. This had to
be determined empirically in most cases. One percent paraformaldehyde adjusted to 360 mOsm
with cacodylate buffer and applied for about 30 minutes maintains the majority of antigens we are
interested in lung tissue. Perhaps the most critical element of a morphometric study is that the
composition of the fixative, dehydrating and embedding solutions be constant. This consideration
should extend to embedding and sectioning tissue from all experimental groups at the same time.
AIRWAY MICRODISSECTION: The approach we have used for precise sample selection is to
employ airway microdissection with a specific binary numbering system.65 This can be applied to
lungs preserved by a variety of methods. Airway microdissection has been used in lungs inflated
at a standard air pressure and fixed by perfusion of the pulmonary vascular bed with aldehyde
fixatives 66 We have also employed specimens that have been frozen after expansion with air to
TLC and lyophilized in a freeze-drying chamber. Beginning at the lobar bronchus, the
intrapulmonary airways and accompanying parenchyma are split down the long axis of the largest
daughter branch or down the axial pathway of the primary airway. An attempt is made to expose
as many minor daughter side branches as possible. Each airway is numbered by a binary system
originally described by Phalen el aL& The binary system is a simple numbering system that
provides a branching history for each segment of the conducting airways. Each time an airway
branches, the larger of the two daughter branches, or the one with the smallest angle of deviation
from tHe long axis, is designated with the number one. The smaller branch, or the one with the
larger angle of deviation from the previous pathway, is designated with a 0. This number provides
a branching history, the number of generations of branching, and a general size relationship of
12
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' specific levels. From these dissected specimens, samples can be taken from specific locations with
known generation number.
TRACHEOBRONCfflAL AND CENTRIACINAR AIRWAYS
The tracheobronchiaJ airways are uniquely characterized by ciliated pseudostratified
columnar epithelium, a submucosa with glands and cartilage. Since epithelial and interstitial cell
populations vary by airway and show different injury and repair patterns by airway generation,^
it seems only appropriate to focus our attention on airways to individual generations. Airway
microdissection is the only practical way to sample airways and still retain knowledge of the
generation number.65
We will use an example (Fig. 1) of airway microdissection, fractionator, optical disector
and local vertical section method to illustrate how one can estimate, the number of epithelial,
interstitial or inflammatory cells for one airway generation. Airway microdissection uses airway
casts in the appropriate species to select the best plane to select desired airways. Airways are
exposed along the longest axial pathway with as many small branching airways as possible in one
plane. A binary system is used to uniquely record airway branching from the trachea. Once an
airway is selected, both halves are cut out of the lung for trimming and subsampling. The airway
is cut transversely into 2 mm rings. Every third ring is selected using a random start, laid flat with
the luminal surface up, rotated randomly and local vertical sections cut perpendicular to the
epithelial basal lamina (Fig. 1). Every fifth block is selected from the series of rings, and tissue
blocks are embedded and cut in alternating step serial sections (1 and 20 Jim). We use the OVF to
count cells in 20 Jim sections, and 1 \im sections to estimate Vv and Sv with local vertical sections.
With those unbiased measurements, we can calculate the volume of the airway wall and its
components, epithelium and interstitium; the number of epithelial and interstitial cells in the airway;
the total surface area of the airway and number of epithelial or interstitial cells per unit of surface.
Data and calculations related to Figure 1 are given below.
The volume of the fixed, dehydrated embedded airway wall (AW).
V(AW) = f(D x f(2) x f(3) x SV(fi) = 3 x 5 x 900 x (2.5 X 10-7cm3) = 0.003375 cm3
13
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where f(l)....f(3) are the fractions sampled at each level from airway rings (f(l)) to fields (f(3)).
The sums of the individual section/field volumes (V(fi)) are used to estimate the volume of the
airway wall. If we counted 100 epithelial cells in the sections with the optical disector, then we can
estimate the total number of epithelial cells (epce) in the wall of the airway as:
N(epce, AW) = «« x f(2> x f<3> * INce = 3 x 5 x 900 x 100 = 1,350,000
In turn, we can calculate the surface area of the epithelial basal lamina (epbl), a good reference
surface, of the airway indirectly from the Sv of epithelial basal lamina to the volume of the airway
wallas:
Sv(epbl,AW) = 2XI(epbl) / L(AW) = 650 cm2 / cm3
where L(AW) is the total length of the grid line in the reference space (airway wall). The total
surface of the epithelial basal lamina of the airway is simply
S(AW) -Svfcpbl. AW) xV(AW) = 650 cm2/cm3 * 0.003375 cm3 = 2. 1938 cm2
In turn, the number of cells per surface of the airway is calculated as:
NS(ep,AW) = N(epceAW) / S(AW) = 1350000 / 2.1938 cm2 - 6.1538 X itf / cm2
One can also calculate the average volume of an airway epithelial cell as:
V(ep,ce) = VV(epce,AW) / NV(#/AW) = 0.2498 / (4 X H* / cm? ) - 6.24 X lO'lO cm3 = 624 ^
where
Nv(WAW) = N(epce,AW) / V(AW) = 1350000 / 0.003375 cm3 = 4 X 10» / cm3
With these calculations, one can detect hyperplasia, hypertropy and differential growth of airways
under numerous experimental or disease conditions.
DISCUSSION
Ozone is well recognized as an oxidant injurant to the mammalian respiratory system. A
pertinent question, particularly considering the recent efforts to assess the costs of bringing major
metropolitan areas in the United States into compliance with the current NAAQS for ozone at
0.12ppm, is the level of risk posed to human beings by exposure to current ambient levels of
ozone. Most of the assessment of health risks and health effects of ozone have been based on
extrapolation of findings reported using laboratory rats. Comparisons of quantitative measures of
14
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cellular changes occurring in 2 of the 3 target zones within the respiratory system demonstrate a
substantial difference in the sensitivity of rats and nonhuman primates. The nonhuman primate
appears to be at least 1 order of magnitude more sensitive at low-level concentrations of ozone than
is the laboratory rat.
The assumption that all species have the same susceptibility to injury from toxic substances
such as ozone is one of the primary difficulties complicating attempts to extrapolate lexicological
data from damage in rats to assessment of risks for human health. Clearly the respiratory system
in various orders of mammals is substantially different in architectural and cellular composition:
including the nasal cavity, the tracheobronchial airways and the centriacinar region of the lung. All
three of these regions are targets for oxidant injury from ozone. Recently quantitative information
became available that allowed direct comparisons, using the same types of morphometric
measurements, of effects of inhalation of ozone on the respiratory system of species of different
orders (Rodentia and Primates). These studies indicate that there are both topographic and cellular
differences between the rat and macaque monkey.
This review suggests that nonhuman primates have less ability to protect their respiratory
system, at least from a cellular perspective, than do rats (i.e., their cells are inherently more
susceptible to ozone-induced injury than are those of the rat). The nasal cavity that could be
expected to remove less ozone in the primate than in the rat is more affected at lower concentrations
of ozone in the primate. Similarly, the centriacinar region of primates is more susceptible to lower
concentrations of ozone when compared with rats by an order of magnitude. What these
comparative findings imply for an assessment of risks to humans from inhalation of ambient
concentrations of ozone is still questionable. It is reasonable to conclude that basing the
assessment of human health risk on studies without nonhuman primates and unbiased
morphometric methods would at best grossly underestimate the potential susceptibility of humans
• to chronic lung injury from ambient concentrations of ozone. It should be noted that the new
generation of designed-based, morphometric methods will allow more precise comparisons to be
made in studies of ozone-induced injury in experimental animals and can now provide the essential
15
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data required for a more accurate assessment of the risk from ozone inhalation on human health.
REFERENCES
1. H. Bravo-Alvarez, G. Roy-Occtia, R. Sosa, R. Torres, "Tendencia del problema del la
contaminacion atmosferica por ozono en la zona suroeste de la ciudad de Mexico
(tendency of the air pollution problem in south-west Mexico City)", Memoirs of fte'
^.ve.nth national Congress of Sanity and En-—-"Tl1 P"?ineering. (Oaxaca, Mexico,
September 1990).
2. L. Calderon-Garciduenas, A. Osorno-Velazquez, H. Bravo-Alvarez, Delgado^Chavez, R.
Barrios-Marquez, "Histopathologic changes of the nasal mucosa in southwest
metropolitan Mexico City inhabitants", Am. J. Pathol. 140, 225-232 (1992).
3. L. W. Schwartz, D. L. Dungworth, M. G. Mustafa, B. K. Tarkington, W. S. Tyler,
"Pulmonary responses of rats to ambient levels of ozone: Effects of 7-day intermittent or
continuous exposure", Lab. Invest. 34,565-578 (1976).
4. C. G. Plopper, D. L. Dungworth, W. S. Tyler, C. K. Chow, "Pulmonary alterations in
rats exposed to 0.2 and 0.1 ppm ozone: A correlated morphological and biochemical
study", Arch. Environ. Health 34, 390-395 (1979).
5. G. A. Boorman, L. W. Schwartz, D. L. Dungworth, "Pulmonary effects of prolonged
ozone insult in rats: Morphometric evaluation of the central acinus", Lab Invest. 43,108-
115 (1980).
6. W. L. Castleman, D. L. Dungworth, L. W. Schwartz, W. S. Tyler, "Acute respiratory
bronchiolitis: An ultrastructural and autoradiographic study of epithelial cell injury and
renewal in rhesus monkeys exposed to ozone", Am. J. Pathol. 98, 811-840 (1980).
T. B. E. Barry, F. J. Miller, J. D. Crapo, "Effects of inhalation of 0.12 and 0.25 parts per
million ozone on the proximal alveolar region of juvenile and adult rats", Lab Invest 53,
692-704 (1985). •
16
-------
8. L. E. Fujinaka, D. M. Hyde, C. G. Plopper, W. S. Tyler, D. L. Dungworth, L. O.
Lollini, "Respiratory bronchiolitis following long-term ozone exposure in bonnet
monkeys: A morphometric study", Exp. Lung Res. 8, 167-190 (1985).
9. R. K. Moffatt, D. M. Hyde, C. G. Plopper, W. S. Tyler, L. F. Putney, "Ozone-induced
adaptive and reactive cellular changes in respiratory bronchioles of bonnet monkeys",
Exp. Lung Res. 12, 57-74 (1987).
10. J. R. Harkema, C. G. Plopper, D. M. Hyde, J. A. St. George, D. W. Wilson, D. L.
Dungworth, "Response of the Macaque nasal epithelium to ambient levels of ozone: A
morphologic and morphometric study of the transitional and respiratory epithelium", Am.
J. Pathol. 128, 29-44 (1987).
*
11. B. C. Barr, D. M. Hyde, C. G. Plopper, D. L. Dungworth, "Distal airway remodeling in
rats chronically exposed to ozone", Am. Rev. Respir. Dis. 137,924-938 (1988).
12. W. S. Tyler, N. K. Tyler, J. A. Last, M. J. Gillespie, T. J. Barstow, "Comparison of
daily and seasonal exposures of young monkeys to ozone", Toxicology 50, 131-144
(1988).
13. J. R. Harkema, J. A. Hotchkiss, R. F. Henderson, "Effects of 0.12 and 0.80 ppm ozone
on rat nasal and nasppharyngeal epithelial mucosubstances: Quantitative histochemistry",
Toxicol. Pathol. 17, 525-535 (1989).
14. M. Lippmann, P. Lioy, G. Leikauf, "Effects of ozone on the pulmonary function of
children", Adv. Mod. Environ. Toxicol. 5, 423-446 (1983).
15. P. J. Lioy, T. A. Vollmuth, M. Lippmann, "Persistence of peak flow decrement in
children following ozone exposures exceeding the National Ambient Air Quality
Standard", J. Air Pollut. Control Assoc. 35, 1068-1071 (1985).
16. W. F. McDonnell, R. S. Chapman, M. W. Leigh, G. L. Strope, A; M. Collier,
"Respiratory responses of vigorously exercising children to 0.12 ozone exposure", Am.
Rev. Respir. Dis. 132, 875-879 (1985).
17
-------
17.
18.
19.
20.
21.
22.
23.
24.
25.
S. I. Gibbons, W. C. Adams, "Combined effects of ozone exposure and ambient heat on
exercising females" J. Appl. Physiol. 57, 450-456 (1984).
E. L. Avol, W. S. Linn, t. G. Venet, D. A. Shamoo, J. D. Hackney, "Comparative
respiratory effects of ozone and ambient oxidant pollution exposure during heavy
exercise", J. Air. Pollut Control Assoc. 74, 804-809 (1984).
F. J. Kulle, L. R. Sauder, J. R. Hebel, M. D. Chatham, "Ozone response relationships
in healthy nonsmokers", Am.' Rev. Respir. Dis. 132, 36-41 (1985).
H. Gong Jr., P. W. Bradley, M. S. Simmons, D. P. Tashkin, "Impaired exercise
performance and pulmonary function in elite cyclists during low-level ozone exposure in a
hot environment", Am. Rev. Respir. Dis. 134, 726-733 (1986).
D. M. Spektor, M. Lippmann, G. D. Thurston, P. J. Lioy, J. Stecko, G. O'Connor, E.
Garshick, F. E. Spelzer, C. Hayes, "Effects of ambient ozone on respiratory function in
healthy adults exercising outdoors", Am. Rev. Respir. Dis. 138, 821-828 (1988).
F. J. Stephens, M. F. Sloan, M. J. Evans, G. Freeman, "Early response of lung to low
levels of ozone", Am. J. Pathol. 74, 31-58 (1974).
C. G. Plopper! C. K. Chow, D. L. Dungworth, M. Brummer, T. J. Nemeth, "Effect of
low level of ozone on rat lungs. II. Morphological responses during recovery and re-
exposure", Exp. Mol. Pathol. 29, 400-41 1 (1979).
C. K. Chow, C. G. Plopper, M. Chiu, D. L. Dungworth, "Dietary vitamin E and
pulmonary biochemical and morphological alterations of rats exposed to 0.1 ppm ozone",
Envir. Res. 24, 315-324 (1981).
U. S. Environmental Protection Agency. Air Polity Criteria for r>7(W- and Other
Oxidants. Volume I, (U. S. Environmental Protection Agency, Research
Triangle Park, 1986).
18
-------
28.
26. U. S. Environmental Protection Agency. Summary of Selected New Information "H
Effects of Ozone on Health and Vegetation: Draft Supplement to Air Quality Cri^ri^ for
Ozone and Other Photochemical Oxidants. (U. S. Environmental Protection Agency,
Research Triangle Park, 1988).
27. J. R. Harkema, C. G. Plopper, D. M. Hyde, J. A. St. George, D. W. Wilson, D. L.
Dungworth, "Response of macaque bronchiolar epithelium to 0.15 and 0.30 ppm ozone",
Am. J. Pathol. (1993), in press.
R. P. Bolender, D. M. Hyde, R. T. DeHoff, "Quantitative morphology of the lung: A
new generation of tools and experiments for organ, tissue, cell, and molecular biology",
J. Appl. Physiol.: Cell and Mol. Biol. (1993) in press.
H. J. G. Gundersen, E. B. Jensen, "The efficiency of systematic sampling in stereology
and its prediction", J. Microsc. 147, 229-263'(1987).
S. Ogbuihi and L. M. Cruz-Orive, "Estimating the total number of lymphatic valves in the
infant lung with the fractionator", J. Microsc. 158,19-30 (1990).
E. R. Weibel, Stereological Methods. Vol I. Practical Methods for Biological
Morphometrv. (Academic Press, New York, 1979), pp. 3-7, 106-108, 139-150, 218-
223, 322-331, 354-379.
32. D. M. Hyde, D. Orthoefer, D. Dungworth, W. Tyler, R. Carter, H. Lum,
"Morphometric and morphologic evaluation of pulmonary lesions in beagle dogs
chronically exposed to high ambient levels of air pollutants", Lab. Invest. 38, 455-469
(1978).
33. J. G. Heidsiek, D. M. Hyde, C. G. Plopper, J. A. St. George, "Quantitative
bistochemistry of mucosubstance in tracheal epithelium of the macaque monkey", J.
Histochem. Cytochem. 35, 435-442 (1987).
34. L. M. Cruz-Orive, "The use of quadrates and test systems in stereology, including
magnification corrections", J. Microsc. 125, 89-102 (1982).
29.
30.
31.
_
19
-------
35'.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
A. J. Baddeley, H. J. G. Gundersen, L. M. Cruz-Orive, "Estimation of surface area
from vertical sections", J. Microsc. 142,259-276 (1986).
H. J. G. Gundersen, "Notes on the estimation of the numerical density of arbitrary
profiles: the edge effect", J. Microsc. Ill, 219-223 (1977).
D. M. Hyde, D. J. Magliano, E. Reus, N. K. Tyler, S. Nichols, W. S. Tyler,
"Computer-assisted morphometry: Point, intersection, and profile counting and three-
dimensional reconstruction", Micros. Res. Tech. 21, 262-270 (1992).
R. P. Bolender, J. Charleston, "Software for counting cells and estimating structural
volumes with the optical volume fractional", Micros. Res. Tech. (1993), in press.
H. J. G. Gundersen, "Stereology of arbitrary particles. A review of unbiased number and
size estimators and the presentation of some new ones, in memory of William R.
Thompson", J. Microsc. 143, 3-45 (1986).
H. J. G. Gundersen, P. Bagger, T. F. Bendtsen, S. M. Evans, L. Korbo, N. Marcussen,
A. Moller, K. Nielsen, IR. Nyengaard, B. Pakkenberg, F. B. Sorensen, A. Vesterby,
M. J. West, "The new stereological tools: disector, fractionator and point sampled
intercepts and their use in pathological research and diagnosis", Acta. Pathol. Microbiol.
Immunol. Scand. 96, 857-881 (1988).
R. T. DeHoff, "Quantitative serial sectioning analysis: Preview", J. Microsc. 131, 259-
263 (1983).
D. C. Sterio, "The unbiased estimation of number and sizes of arbitrary particles using
the disector", J. Microsc. 134,127-136 (1984).
M. J. West, L. Slomianka, H. J. G. Gundersen, "Unbiased stereological estimation of the
total number of neurons in the subdivisions of rat hippocampus using the optical
. fractionator", AnatRec. 231,482-492 (1991).
H. J. G. Gundersen, "The nucleator", J. Microsc. 151, 3-21 (1988).
L. M. Cruz-drive, "Particle number can be estimated using a dissector of unknown
thickness: The Selector", J. Microsc. 145,121-142 (1987).
20
-------
46. H. J. G. Gundersen, E. B. Jensen, "Stereological estimation of the volume-weighted
mean volume of arbitrary particles observed on random sections", J. Microsc. 138, 127-
142 (1985).
47. F. Gittes, R. P. Bolender, "Counting cell nuclei with random sections: the effect of-shape
and size", Micron. Microsc. Acta 18, 59-70 (1987).
48. F. Gittes, "Estimating mean particle volume and number from random sections by
sampling profile boundaries", J. Microsc. 158, 1-18 (1990).
49. 'V. Howard, S. Reid, A. J. Baddeley, A. Boyde, "Unbiased estimation of particle
density in the tandem scanning reflected light microscope", J. Microsc. 138, 203-212
(1985).
50. E. Thomson, "Quantitative microscopic analysis", J. Geol. 38, 193 (1930).
51. E. R. Weibel, Stereological Methods. Vol. 2. Theoretical Foundations. (New York,
Academic Press, 1980).
52. C. S. Smith, L. Guttman, "Measurement of internal boundaries in three-dimensional
structures by random sectioning", Trans AIME 197, 81-111 (1953).
53. S. L. Tomkeieff, "Linear interceps, areas and volumes", Nature 155, 24 (1945).
54. A. M. Gokhale, "Unbiased estimation of curve length in 3D using vertical slices", J.
Microsc., 159, 133-141 (1990).
55. A. M. Gokhale, "Estimation of length density Ly from vertical slices of unknown
thickness", J. Microsc. 167, 1-8 (1992).
56. L. M. Cruz-Orive, "Best linear unbiased estimators for stereology", Biometrics 36, 595-
605(1980).
57. H. Elias H, D. M. Hyde, A Guide to Practical Stereology. (Basel, S. Karger A.G.,
1983) pp. 16-24, 57-82.
58. H. J. G. Gundersen, R.Osterby, "Optimizing sampling efficiency of Stereological studies
in biology, or "Do More Less Well!"", J. Microsc. 121, 65-73 (1981).
21
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59. W. Nicholson, "Estimation of linear properties of particle size distributions", Biometrika
57, 273-294 (1970).
60. W. Nicholson, "Application of statistical methods in quantitative microscopy", J.
Microsc. 113,223-239(1978).
61. L. Chang, R. R. Mercer, K. Pinkerton, J. D. Crapo, "Quantifying lung structure.
experimental design and biologic variation in various models of lung injury", Am. Rev.
Respir. Dis. 143, 625-634 (1991).
62. E. E. Schneeberger-Keeley, M. J. Kamovsky, "The ultrastructural basis of alveolar-
capillary membrane permeability to peroxidase used as a tracer", J. Cell Biol. 37, 781-
793 (1968).
63. D. Sandoz, G. Nicolas, M. Laine, "Two mucous cell types revisited after quick-freezing
andcryosubstitution", Biol. Cell 54, 79-88 (1985).
64. O. Mathieu, H. Claassen, and E. R. Weibel, "Differential effect of glutaraldehyde and
buffer osmolarity of cell dimensions: A study on lung tissue", J. Ultrastruc. Res. 63,20-
34 (1978).
65. C. Gr Plopper, A. T. Mariassy, L. O. Lollini, "Structure as revealed by airway
dissection: A comparison of mammalian lungs", Amer. Rev. Respir. Dis. 128, S4-S7
(1983).
66. J- Gil, E. R. Weibel, "Extracellular lining of the bronchioles after perfusion fixation of rat
lungs forelectron microscopy", Anat. Rec. 169,185-200 (1971).
67. R. F. Phalen, H. C. Yeh, G. M. Schum, O. G. Raabe, "Application of an idealized
model to morphometrv of the mammalian tracheobronchial tree", Anat'Rec. 190,167-76
(1978).
68. D. M. Hyde, W. C. Hubbard, V. Wong, R. Wu, C. G. Plopper, "Ozone-induced
tracheobronchial epithelial injury: Relationship to granulocyte emigration in the lung", Am.
J. Respir. CellMol. Biol. 6,481-497 (1991).
22
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FIGURE LEGENDS
Figure 1. An illustration of airway selection, sampling and sectioning for estimating the total
number of cells in the airway, the mean volume of the cells and the number per surface of the
airway. Airway microdissection is used to exposed airways along the longest axial pathway with
as many small branching airways as possible in one plane. An airway is selected using both sides
of the dissected lobe, and cut into 2 mm transverse rings. Every third ring (f = 3) is selected by
stratified sampling with a random start, laid flat with the luminal surface up, rotated randomly and
local vertical sections cut perpendicular to the epithelial basal lamina. Every fifth block (f = 5) is
selected from the series of rings, it is embedded and cut in alternating step serial sections (20 [im
and 1 fim) and with a random start every ninehundreth section/field is selected (f = 900) to. estimate
Ny using the OVF method to count cells in a known volume, and Sy and Vy using point and
intersection counting. The "f' is for fraction which represent the fraction used for sampling and to
estimate total values for the sampled airway. Note that Sy requires a cycloid grid and a local
vertical section.
ACKNOWLEDGMENTS: This work was supported in part by NIH grants HL28978,
ES00628, and DRR00169.
FOOTNOTES
a. Cavalieri method is named for the Italian mathematician Bonaventura Cavalieri (1598-1647),
who first proposed the method for estimating volume.
23
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X
X,
Vertical xa&
20
Nv
Sv
Vv
-------
Daniel S. Marsman D.V.M., Ph.D.
Pathology Branch, Environmental Toxicology Program
Division of Intramural Research, NIEHS
Dr. Marsman's work has centered on understanding the underlying mechanisms of chemical
carcinogenesis. Prior to his current position at NIEHS he served as a postdoctoral fellow
at CUT, investigating the rodent hepatocarcinogenicity of the peroxisome proliferating
chemicals. His published work has included characterization of the cell proliferative and
promotional effects of chemicals and the persistence and metastatic potential of chemically
induced hepatocellular adenomas and carcinomas. A veterinary (D.V.M., Michigan State
University) pathologist (Ph.D., University of North Carolina at Chapel Hill), his training has
also included an externship to Khartoum, Sudan, investigating Onchocerciasis ('River
Blindness'). Currently, he is an Expert Pathologist for the Environmental Toxicology
Program, NIEHS. In addition to his responsibilities to the National Toxicology Program
(NTP), he has recently been named team leader for developing and implementing a research
strategy at NIEHS characterizing the potential risk of the peroxisome proliferators.
-------
APOPTOSIS AND CHEMICAL CARCINOGENESIS
Daniel S. Marsman and J. Carl Barrett,
NIEHS, P.O. Box 12233, Research Triangle Park, NC 27709
ABSTRACT
Long recognized as a normal component of organogenesis during development,
apoptosis ('programmed cell death') has recently been implicated in alterations of cell
growth and differentiation. Tissue homeostasis is normally maintained by a balance
between cell division and cell death, with apoptosis often functioning in complement to
cell growth. Thus, antithetical parallels in chemical carcinogenesis can be drawn between
apoptosis and the proliferate events more commonly addressed. As enhanced cell
replication may contribute to an increased frequency of initiation, apoptosis within a tissue
may counteract chemical carcinogenesis through loss of mutated cells. Many strong
carcinogens act as tumor promoters, selectively expanding an initiated cell population
advantageously over surrounding cells. Similarly, chemicals with a selective inhibition of
apoptosis would offer a growth advantage, while chemicals causing 'selective' apoptosis
within mutated cells would be expected to have an anticarcinogenic effect. Selective
apoptosis, in concert with cell-specific cell replication, may explain the unique promoting
effects of different'cardrwgens such as the peroxisome proliferating chemicals,
phenobarbtal, and TCDD. Cell turnover, both cell growth and cell death, is central to the
underlying processes of chemically induced carcinogenesis in animals, and the relevance
of these effects to man.
-------
Apoptosis: systematic, gene-directed cell daath
During the development of an organism considerable remodeling takes place,
involving not only cell proliferation and differentiation, but highly organized apoptosis, or
programmed cell death. No where has this been more elegantly demonstrated than in
the nematode Caenorhabclitis eleaans where, in route to the adult organism of 959 cells,
precisely 131 cells systematically die (1,2). In this animal model, the genetic control of
apoptosis involves unique sets of controlling genes. Some apparently function in
negative roles, controlled by gene products in which gain of function mutations prolong
the life of the cell, and deletions result in cell death. The targets of these inhibitors are
the activators of the orderly and systematic process of cell death (1). Thus, the apoptotic
process is highly active, with numerous molecular events, some of which can be
recognized histologically. , .
Apoptosis, as recognized in liver and other tissues, has been well described
elsewhere (3,4). In hepatocytes, these stages histologically involve cytoplasmic basophilia
and nuclear condensation, eosinophilia and cytoplasmic condensation, and finally nuclear
and cellular fragmentation and dissolution. Typically, when taking place within a solid
organ, apoptosis also characteristically involves phagocytosis-by neighboring cells,
however luminal sloughing or 'shedding' also occurs in epithelial organs such as the
kidney (5). Early cytoplasmic changes are considered an indicator of the active
involvement of specific gene products used in the orderly demise of the cell, however to
date few definitive nonmorphological markers exist. While active transcription of a few
genes is occurring, the majority of cellular functions are diminishing, consistent with the
observed nuclear and cytoplasmic condensation. Coinciding with nuclear condensation
is the induction of an endonuclease, cleaving chromatin at internucleosomal DNA linker
-2-
-------
regions and generating a characteristic oligonucleosomal ladder (-185 base-pair units).
observable following agarose gel electrophoresis (6). The appearance of cytoplasmic
eosinophllia/condensation coincides with fibrin organization, arranged concentrically
around the periphery to collapse the cell on itself. Phagocytosis by neighboring cells
does occur, with apoptotic cells potentially utilizing cell-surface signals involving the
vitroneotin receptor (7,8). Cellular blebbing, fragmentation, and packaging of detergent-
insoluble structures appears to involve a tissue transglutaminase, however the
significance and generality of this marker for apoptosis remain to established (9,10). Final
degradation of the apoptotic remnant or 'body' occurs within phagocytic cells and
disappearance of the apoptotic body may occur as rapidly as a few hours (11).
However, smaller degradative membrane products (recognizable as lipofuscin) may
remain for a considerable period of time (12).
Several examples of cell loss or irreversible growth arrest occur in mammalian
systems besides apoptosis, including terminal differentiation, senescence, and necrosis
and will only be mentioned briefly here (9). While markedly deferent morphologically and
relative to their induction and genetic control, all of these forms of cell loss effectively
result in exciusion of the cell(s) from the replicating population. Terminal differentiation
is associated wfth the expression of a specialized function or product of a tissue. Cellular
senescence, is tightly controlled.by genes tnat are actuated or whose functions become
manned at the end of the l«e span of the cell. Defects in the function of these gene
products allow cells to escape the route to senescence and are thought to be 'immortal'.
Senescence may be one mechanism by which tumor suppressor genes operate (13,14).
Necrosis, in contrast to apoptosis, is not a orderly cellular process but rather the
disorganized death of a cell. Random dissolution of all cellular components, the release
-3-
-------
of cellular constituents, and often the induction of an overt inflammatory response are all
characteristics of necrosis, which are not typically associated with apoptosis (15).
Apoptosis, even when involving a considerable portion of an organ or tissue, is not
associated with an inflammatory process (16), although apoptosis and necrosis may co-
exist (17).
Despite considerable evidence that apoptosis is tightly controlled, only a few genes
have been definitively shown to be involved. The cellular signalling and genetic control
of apoptosis have been recently reviewed elsewhere and will not be discussed here (9).
Candidate genes which have been identified include bcl-2, and the tumor suppressor
gene P53. Signalling factors suggested have included cytosolic Ca2+, IL-2, iL-3, and CSF.
Paradoxically, factors such as cytosolic Ca2+, and induction of c-fos and c-myc immediate
early genes are shared by both the cell death and cell proliferation signalling pathways.
Initiated cells: impact of non-specific apoptosis and enhanced cell replication.
Carcinogenesis has been described in risk assessment paradigms as a multistage
phenomenon involving multiple, discrete genetic mutations. More recently mathematical
descriptions of this phenomenon have become increasingly biologically based,
recognizing the potential contributions of both cell proliferation and cell loss (18). These
models have been constructed to describe cancers involving two heritable changes ('M1'
and 'M2'; Figure 1), recognizing however that while useful for this discussion this is likely
an over-simplification.
-4.
-------
Fig. 1 Biologically Based Cancer Model.
Enhanced cell replication has been implicated as a risk factor in several cases of
chemically induced carcinogenesis (19,20). In these instances, it is stated or implicitly
implied that background errors in DNA replication result in 'spontaneous' initiation of a
cell. Theoretically, in cases where either the replicating cell population ('N'; Figure 1) is
significantly increased or the replication rate of this population ('RO') far exceeds baseline
levels, the normally rare probability of a heritably altered cell (T) to form and persist is
increased. While this 'probability-mutagenesis' is often discussed in relation to cell
proliferation, it is often neglected that a complementary role may be played by cell loss.
Thus, inhibition of apoptosis may similarly increase mutational frequencies by increasing
the- population at-risk (N or I populations). However, while decreased cell loss may
impact on carcinogenesis by the scenario addressed above, apoptosis-inhibition may
itself impact directly on mutational frequencies (M1 and/or M2), as discussed below.
Inhibition of apoptosis is reversible.
One important characteristic of the inhibition of apoptosis by chemicals is
reversibility. A synchronized wave of apoptosis occurs within the liver following the
-5-
-------
withdrawal of numerous hepatic mitogens/hepatocarcinogens, indicative of homeostatic
reversal of 'apoptosis-inhibition' (16,21-24). Induction of apoptosis following withdrawal
of endogenous hormones or growth factors has similarly been demonstrated (9,25,26).
This regression is considered a normal homeostatic mechanism but suggests that direct
evidence for apoptosis-inhibition is not evident in retrospective examinations. Within the
context of chemical carcinogenesis this observation also suggests that apoptosis-
inhibition by chemicals may be untraceable by examination of the end product, the tumor.
Apoptosis and initiation. •
The blockade of ceil death becomes even more intriguing when one considers that
aborted cell death may itself lead to initiation of the carcinogenic process. If apoptosis-
resistance is due to blockade of a stage of apoptosis after DMA fragmentation has begun
(6), outcomes of genomic instability such as deletions, frame shift mutations, and genetic
recombinants may increase. .In addition, cells that are normally programmed to die, due
to age or accumulation of spontaneous genetic damage, may persist and even replicate
to form a heritably altered progeny if apoptosis is inhibited. Thus, apoptosis-inhibited
cells may not only have a growth advantage over their neighboring cells, but possess an
error-prone mutator phenotype, hypothesized to be a critical event in some forms of
chemical carcinogenesis (27). As mentioned above, apoptosis-inhibition is often through
a reversible alteration of signal transduction. In these instances chemicals could act as
indirect initiators, with no demonstrable form of DNA reactivity. A feature of many diverse
hepatocarcinogens is indeed their lack of DNA reactivity; these chemicals are not only
strong promoters (28-30), but are also carcinogenic in long term feeding studies in the
absence of an initiator (31-33).
-6-
-------
'Prnmotional factors' in tha nontext of a single mutated.celL
Following the mutation of a single cell, tumor promotional factors (R1 and D1;
Figure 1) become critical to the 'survival' of this initiated cell population (I). Survival of a
single initiated cell, assuming that cell loss is comparable to the surrounding tissue, can
be described as D1-DO. Clearly, apoptosis-inhibition directed at the initiated cell
(D1
-------
subpopulations of altered cells with a selective growth advantage over surrounding cells.
Enhanced hepatocyte apoptosis has been observed in the livers of rats fed the strong
*
rodent hepatocarcinogen, Wy-14,643 (12). In cases such as this, resistant hepatocytes
would have an accelerated growth advantage, with resistant cells potentially recruited into
cell proliferation due to the surrounding cell loss.
Cell-specific apoptosis and lesion regression.
In addition to regression of the liver following the removal of a mitogenic
carcinogen, rapid induction of apoptosis is also observed within proliferative hepatic
adenomas (22-24). A hopeful line of research in cancer chemotherapy is attempting to
exploit apoptosis directed at specific cell-types, to decrease directly tumor size rather
than simply to inhibit further growth. High selectivity is still highly desirable however as
complete loss of the tumor will not likely result unless the tumor is very small (i.e. the size
of I population is near 1; Figure 1), or the efficacy is very high (D1 is significantly greater
than R1). In peroxisome proliferator-treated rats, many of the common histologic markers
indicative of preneoplasia are negative, with numbers of hepatic foci often less in treated
animals than in control (36,37). While this may be simply due to selective expression of
certain phenotypes, the relative decrease of individual hepatic foci may actually represent
loss of initiated cell populations. However, unless apoptosis is directed at specific cells,
only small populations of initiated cells would likely be eliminated. Cell-directed specificity
is not unlikely, as many of the most active promoters are now thought to exert all or some
of their effects through receptor mediated events (eg. dioxins, peroxisome proliferators)
(38,39). Where this selective promotional activity is uniquely specific to a subpopulation
of cells or is of sufficient strength (R1 significantly greater than D1 and/or DO significantly
-8-
-------
greater than D1), promoters take on a very distinct pattern of phenotypic expression.
Apoptosis and tumor progression.
A hallmark of tumor progression is the persistence and autonomous growth of a
tumor, despite the removal of the inciting agent. As mentioned above, escape from
cellular senescence or apoptotic-inhibition may lead to unrestrained growth of cells or
immortality (9). In hepatocellular tumors induced by the peroxisome proliferating chemical
Wy-14,643, tumor progression is particularly evident. Despite the induction of numerous
large hepatocellular adenomas by Wy-14,643, withdrawal of this carcinogen results in few
persistent tumors (Figure 2)1. In contrast, hepatocellular carcinomas induced by 52
weeks of treatment are no longer dependent on Wy-14,643 for either growth or
metastasis. Escape from senescence or loss of apoptotic control may be one
characteristic of the induced hepatocellular carcinomas.
Adenomas
Carcinomas
100
40
12 37 "
Wy-14,643
•Continuous1
37/»
.104 -104 -104
Wy-14,643
•Stopped*
0.25
0.00
Wy-14,643
•Continuous'
Fig. 2 BtotogM Potent*, of Hepatoce.lu.ar
-9-
-------
Conclusion
Human health assessments of cancer risk associated with exposure to
environmental chemicals are best when a full understanding of the carcinogenic
mechanism in animals and the relevance of this response to man are known. To this
end, understanding the chemical induction and inhibition of apoptosis becomes a
necessary component in our understanding of the carcinogenic mechanism and
extrapolation of this data to humans.
1 Marsman, D.S. and Popp.J.A. (1993) Unpublished observations.
REFERENCES
1.
2.
3.
4.
5.
6.
EIlis.R.E., Yuan.J.Y. and Horvitz.H.R. (1991) Mechanisms and functions of cell
death. Annu. Rev. Cell Biol. 7:663-698.
Raff.M.C. (1992) Social controls on cell survival and cell death. Nature 356:397-400.
Bursch.W., Taper,H.S., Lauer.B. and Schulte-Hermann.R. (1985) Quantitative
histological and histochemical studies on the occurrence and stages of controlled
cell death (apoptosis) during regression of rat liver hyperplasia. Virchows Arch.
[Cell Pathol.] 50:153-166.
Kerr,J.F.R., Wyllie,A.H. and Currie,A.R. (1972) Apoptosis: A basic biological
phenomenon with wide-ranging implications in tissue kinetics. Br. J. Cancer
26:239-257.
^Ledda-Columbano.G.M., Columbano.A., Coni.P., Faa,G. and Pani.P. (1989) Cell
'deletion by apoptosis during regression of renal hyperplasia. Amer. J. Pathol.
135:657-662.
Arends.M.J., Morris.R.G. and WyllieAH. (1990) Apoptosis: the role of the
endonuclease. Amer. J. Pathol. 136:593-608.
-10-
-------
8
9
"•
-------
19.
20.
21.
Grasso.P., Sharratt.M. and Cohen.AJ. (1991) Role of persistent, non-genotoxic
tissue damage in rodent cancer and relevance to humans. Annu. Rev. Pharmacol
Toxicol., 31:253-287.
Ames.B.N. and Gold.LS. (1990) Chemical carcinogenesis: too many rodent
carcinogens. Proc. Natl. Acad. Sci. USA, 87:7772-7776.
Tomei.LD., Kanter.P. and Wenner.C.E. (1988) Inhibition of radiation-induced
apoptosis jn vitro by tumor promoters. Biochem. Biophys. Res. Comm. 155:324->
331.
22. Bursch.W., Lauer.B., Timmermann-Trosiener.l., Barthel.G., Schuppler.J. and
Schulte-Hermann,R. (1984) Controlled death (apoptosis) of normal and putative
preneoplastic cells in rat liver following withdrawal of tumor promoters.
Carcinogenesis 5:453-458.
23. Gerbracht.U., Bursch.W., Kraus.P., Putz.B., Reinacher.M., Timmermann-Trosiener.l.
and Schulte-Hermann.R. (1990) Effects of hypolipidemic drugs nafenopin and
clofibrate on phenotypic expression and cell death (apoptosis) in altered foci of rat
liver. Carcinogenesis 11:617-624.
24. Schulte-Hermann.R., Timmermann-Trosiener,!., Barthel.G. and Bursch.W. (1990)
DMA synthesis, apoptosis, and phenotypic expression as determinants of growth
of altered foci in rat liver during phenobarbital promotion. Cancer Res. 50:5127-
5135.
25. Rotello.R.J., Hocker.M.B. and Gerschenson.LE. (1989) Biochemical evidence for
programmed cell death in rabbit uterine epithelium. Am. J. Pathol. 134:491 -495.
26. Azmi.T.I. and O'Shea.J.D. (1984) Mechanism of deletion of endothelial cells during
regression of the corpus luteum. Lab. Invest. 51:206-217.
27. Loeb.LA. (1991) Mutator phenotypes may be required for multistage
carcinogenesis. Cancer Res. 51:3075-3079.
28. Cattley, R. C., and Popp, J. A. (1989) Differences between the promoting activities
of the peroxisome proliferator Wy-14,643 and phenobarbital in rat liver. Cancer
Res. 49:3246-3251.
29. Xu,Y., Maronpot,R. and Pitot.H.C. (1990) Quantitative stereologic study of the
effects of varying the time between initiation and promotion on four histochemical
markers in rat liver during hepatocarcinogenesis. Carcinogenesis 11:267-272.
30. Pitot,H.C., Goldsworthy.T., Campbell.HA and PolandA (1980) Quantitative
evaluation ' of the promotion, by 2,3,7,8-tetrachlorodibenzo-p-dioxin of
hepatocarcinogenesis from diethylnitrosamine. Cancer Res. 40:3616-3620.
-12-
-------
31
32.
33.
34.
35.
36.
37.
M «m«n n 9 Cattlev R C Conway, J. G., and Popp, J. A. Relationship of
[4 chloro-6-(2,3-xylidino)-2-pyrimidinylthio]acet,c ac,d (Wy-14,643) in rats. Cancer
Res., 48: 6739-6744, 1988.
Rossi L Ravera M., Raped, G., and Santi, L. Long-term administration of DDT
or phenobarbital-Na in Wistar rats. Int. J. Cancer, 19: 179-185, 1977.
H. iff IE Salmon A G Hooper.N.K. and Zeise.L (1991) Long-term carcinogenesis
studie'lfo?2T7^ach.o?odibenzo-p-dioxin and hexach.orodibenzo-p-d.ox.ns.
Cell biol. Toxicol. 7:67-94.
Res. 49:6985-6988.
(1977) Rapid emergence of carcinogen-induced
• • *-- -•-- sequential analysis of liver
carcinogenesis. Am. J. Pathol. 88:595-618.
foci within the rat liver. Carcinogenesis 5:41-46.
proliferator Wy-1 4,643. Carcinogenesis 3: 1231 -123d.
39.
4946.
lssemann.1. and Green, & (1990) Activation of a member of the , «jt*J hormone
receptor superfamily by peroxisome prolrferators. Nature 347.645-650.
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BIOGRAPHICAL SKETCH: DR. DAVID MATTIE
Dr. Mattie is a research toxicologist in the Hazard Assessment
Branch, toxicology Division, Occupational and Environmental Health
Directorate, Armstrong Laboratory, Wright-Patterson AFB, OH His
primary area of research interest is in dermal toxicology and risk
assessment. He conducts research to quantitatively assess the
dermal route of exposure for Air Force chemicals and materials. He
is also examining species differences in skin penetration. He has
developed methodology for determining skin:air and stratum
corneum:air partition coefficients for PBPK models with a skin
compartment. s»*.J-n
Dr. Mattie received his undergraduate degree in biology from Quincy
College in Quincy, IL in 1974. His Master of Science Degree in
Biology was received from the University of Dayton in 1977 He
obtained his Doctor of Philosophy Degree in Biology from the
University of Dayton in 1983. He is certified as a Diplomate of
the American Board of Toxicology.
He was Co-Chairperson for the 1992 Toxicology Conference
Applications of Advances in Toxicology to Risk Assessment," as
C°nference P^eedings that appeared in
_
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SIGNIFICANCE OF THE DERMAL ROUTE OF EXPOSURE TO RISK ASSESSMENT
D.R. Mattie, J.H. Grabau, J.N. McDougal
ABSTRACT
SS 3
as compared to inhalation or oral routes
ao
o !
for systemic
permeability uuusuaiii.. ,,^^ r ~~~~' •„!*
development and acquisition.
-------
INTRODUCTION
BACKGROUND
The skin, constituting 10 percent of total human body weight,
acts as the major interface between the carefully regulated
internal environment of the body and the comparatively dry and
potentially hostile external environment. The skin primarily
functions as a barrier. This barrier may still permit entry of
chemical substances into the-body. The potential for
occupational or accidental skin exposure to nonvolatile and
volatile chemicals (both of which may penetrate the barrier of
the skin) requires a better understanding of chemical absorption
through the skin to adequately determine risks of such exposures.
Personnel working in the occupational environment are potentially
exposed to a multitude of chemicals. Maintenance, repair and
fueling operations expose workers to engine oils, lubricants,
fuels, hydraulic fluids, paints and solvents. All of these
classes of compounds present the potential for dermal exposure.
As high as 40% of all occupational illness is related to skin
disease (Wheeler, 1992). For some substances, cutaneous
absorption plays an important role in overall exposure
(Scansetti, Piolatto, and Rubino, 1988). The dermal route of
occupational exposure was found to be the major contributor to
total polychlorinated biphenyls body burden of transformer
maintenance and repair personnel (Lees, Corn, and Breysse, 1987).
The absorbed body dosage of xylene from hand contact with solvent
mixtures over a cumulative period as short as 15 minutes was
greater than that from inhalation over a full shift in auto body
repair shops (Daniell et al., 1992). Even wearing gloves is not
an absolute form of protection as permeation of chemicals through
gloves has been shown to occur (Perkins and Knight, 1988).
Absorption of chemicals through the skin appears to be of greater
significance than previously reported from industry or
epidemiological experience (Grandjean et al., 1988).
The dermal route of exposure may not be as significant as the
inhalation route, but it can contribute to total exposure. For a
highly soluble chemical such as dibromomethane, the body burden
from dermal penetration compared to inhalation for a rat is
approximately 6% (McDougal et al., 1985). If a respirator were
worn but the skin was unprotected, exposure to a soluble chemical
vapor would still occur. A method to compare dermal vapor
exposure to inhalation exposure at the same concentration has
been described as a ratio of input functions for the contribution
of each route of exposure, providing the permeability constant,
surface area of skin exposed and'aveolar ventiliation rate are
known or can be determined (McDougal and Clewell, 1990).
Chemicals in the liquid state must also be considered as many
chemicals exist as a neat liquid or dissolved in a liquid medium
such as water. Concentrations of pure liquid are much greater
than in their vapor form. This results in greater penetration
-------
the
barrier properties of the skin.
Tsuruta (1975) and others have Deported on Jhe percutaneous
'
accurate assessment of actual exposure levels.
cSSSS'for^ScuSneoue absoption in order to assess its
overall potential risk.
ESTIMATES OF DERMAL PENETRATION
Various methods have been used to ^^^fSSiiSy^onstant
chemical to penetrate through t]?ens^n;hjhjb?i™y of a chemical
coefficient for skin is known.
as shown in the following equation for flux.
-------
Flux =
= kp C
where C is the concentration gradient of chemical in the skin
(g/cm , 1 is the skin thickness (cm), k^ is the solubility or
partition coefficent of the chemical in skin (unitless), D is the
diffusion coefficient (cirr/hr), and kp is the permeability
constant (cm/hr).
Physical and chemical properties of chemicals such a's solubility
are important descriptors of skin penetration (Grandjean et al.,
1988; Pershing, Lambert and Knutson, 1989; and Surber et al.
1990). The partition coefficient (PC), a measure of the affinity
of a chemical for tissue, is the ratio of concentrations at
equilibrium between the tissue and an adjacent media, such as
air, water or other vehicle. Various experimental methods have
been reported in the literature for determining partition.
coefficient values for skin. One method uses the octanol/water
partition coefficient as a surrogate for partitioning between the
skin (octanol phase) and the environment or vehicle (water phase)
(Bronaugh and Congdon, 1984; and Kasting, Smith and Cooper,
1987). Octanol/water PC values are typically determined by
shaking the test compound in a mixture containing equal parts of
water and octanol. After sufficient time for equilibration to
occur, the ratio of the amount of test compound in each solvent
is determined {Bronaugh and Congdon, 1984). Hawkins and
Reifenrath (1985) compared octanol/water PC values to the percent
of applied dose of pesticides and steroid hormones after exposure
in vitro through pig and human skin. Kasting, Smith and Cooper
(1987) used octanol/water PC values in a mathematical model to
calculate the flux of chemicals across the skin. Berner et al.
(1988) used octanol/water PC values to confirm skin permeation
rates for a series of chemicals prior to examining the
relationship between the pKa of these chemicals and acute skin
irritation. Octanol/water PC values have been used to calculate
dermal flux for setting a skin notation guideline for a Threshold
Limit Value-Time Weighted Average (TLV-TWA), (Fiserova-Bergerova,
Pierce and Droz (1990). Although the octanol/water partition
coefficient has been used extensively in estimating dermal
penetration, it is an oversimplification of the process of
chemical interaction with the skin. The octanol/water partition
coefficient assumes that skin is homogenous with respect to
octanol.
Surber et al. (1990) measured SC/water and SC/isopropyl myristate
PC values. In their study, partition coefficients were
determined'as a function of equilibration time, initial
concentration of drug in the vehicle, delipidization of stratum
corneum, and source and preparation of stratum corneum. The
partition coefficients were considered as predictors of
percutaneous penetration for the purpose of conducting dermal
-------
risk assessments (Surber et al., 1990).
TIERED APPROACH
use, initial toxicity results, etc.
the endpoints for a Phase II screen.
SKIN: AIR PARTITION COEFFICIENTS
Introduction
Th
was developed in order to measure skin: air PC values.
Material and Methods
CHEMICALS: The following chemicals were used for the
halothane f rom Halocarbon Labs, Inc. (HackensacK, NJ ) ,
isoflurane f rom Anaquest (Madison, WI).
Purina Formula #5008) were available ad libitum. Tne amoien
-------
FIGURE 1. TIERED APPROACH TO DERMAL RISK ASSESSMENT
INITIAL
EXPOSURE
ASSESSMENT
STRUCTURE ACTIVITY RELATIONSHIPS (SAR)
IN VITRO IRRITANCY
PHASE I SCREEN
DERMAL EXPOSURE
IN VIVO ACUTE
SENSITIZ.
IN VITRO
PHASE II SCREEN
IN VITRO
PHASE III SCREEN
CHRONIC BIOASSAYS
SKIN PAINTING
IN VITRO GENOTOXIC
SCREEN
INIT/PROMOTION
PHASE IV SCREEN
ACTUAL
EXPOSURE
ASSESSMENT
DERMAL RISK
ASSESSMENT
INHALATION
RISK ASSESSMENT
HAZARD
ASSESSMENT
-------
temperature was maintained at 22±2°C and light was regulated on a
12-hour-light/dark cycle (starting at 0600 hrs).
SKIN PREPARATION: Dorsal skin of a rat was clipped using an
electric clipper immediately after euthanasia with carbon
dioxide? AftSr collecting the skin, the hypoderims was removed,
the skin was cut into 1 by 0.5 cm strips with a razor blade and
Supelco, Inc., Bellefonte, PA).
PARTITION COEFFICIENT DETERMINATION: Sample vials containing
skin and their corresponding empty reference vo.als w^e incubated
for 10 minutes at 32°C using a vortex evaporator to warm the akin
and vials. After reaching 32°C the caps were brxe fly
aat0ppm.oneor cemia
bag yielded a concentration of 406 ppm in the sample
1
. Qf
wi?h a flfSSionization detector (Model 5890A, Hewlett Packard,
toe reference nd sample vials in a set, sample
compared versus corresponding reference vials using
equation modified from Gargas et al. (I9«y).
^^^
followxng
PC - (reference -ea^ts) ,vlal
(test area cts)( sample volume)
with 41 ppm (0.1 mL from the lOOOOppm air bag) or 102 ppm (0.25
^Irom ?hS ioOOOppm air bag) dibromomethane to examine the
effects of different concentrations on the skin partition
coefficient.
-------
After developing the technique for determining a skin:air PC
value using dibromomethane, the procedure was used for the other
volatile organic chemicals of interest. The time to reach
equilibration was determined for each additional chemical.
Between 16-24 samples were measured to determine skiniair
partition coefficients for these chemicals at 203 ppm (5000 ppm
bag). Four additional samples were measured at 41 ppm (1000 ppm
bag) to confirm that there was no concentration effect.
STATISTICAL ANALYSES: To determine the equilibration time and
compare the effect of different concentrations, PC data was
compared by a one way analysis of variance. Linear regression
and correlation for figures comparing PC values versus
permeability constants and octanol/water PC values were
determined using the line fit procedure in RSI on a mainframe
computer (BBN Software Products Corporation, Cambridge, MA). The
level of significance was accepted at p<0.05.
Results
After incubating samples for various times, the equilibration
time for dibromomethane in skin was determined to be 4 hours.
The skin:air partition coefficent for dibromomethane at 203 ppm
was 68.3+3.1. The skin:air was also determined for 102 and 41
ppm. There was no significant difference between the skin:air PC
values determined at 203, 102, and 41 ppm concentrations (data
not shown).
The skin:air PC values for selected volatile organic chemicals
are shown in Table 1. Approximately 19 samples were analyzed for
each chemical. The most common equilibration time for this group
of volatile chemicals was 4 hours. The skin:air PC values ranged
from 1.9 for hexane to 91.9 for styrene. For each chemical 4
samples were also run at 41 ppm. The value for the skin:air PC
at 41 ppm was always the same as the value at 203 ppm (data not
shown) .
The octanol/water PC values (Leo, Hansch, and Elkins, 1971) were
compared to the corresponding skin:air PC values for 11 of the above
chemicals. There was no correlation (r2=0.09) between the
octanol/water PC values and skin:air PC values (Figure 2).
Permeability constants were available for 9 of the chemicals for
which skin;air PC values were measured in this study (McDougal et
al, 1985; McDougal et al, 1986; and McDougal et al, 1990). There
was good correlation (r^=0.93) between permeability constants and
skin:air PC values (Figure 3).
Discussion
The skin:air PC values were compared to both octanol/water PC values
and permeability constants. Octanol/water PC values have
-------
Table 1. SkinrAir Partition Coefficients for Selected Volatile
Organic Chemicals
CHEMICAL
perchloroethylene
trichloroethylene
benzene
hexane
toluene
xylene
styrene
methylene chloride
carbon tetrachloride
methyl chloroform
halothane
isoflurane
SKIN:AIR PC
( + S.E.)
41.5+1.2
31.8+1.5
34.5+1.9
• 1.9+0.1
43.0+1.8
50.4+1.7
91.9+6.8
13.6+0.5
12.4+0.6
10.8+0.6
10.6+0.7
4.5+0.3
EQUILIBRATION
TIME
n (hours)
16 4
19 4
19 4
18 4
16 4
24 2
20 3
17 2
24 4
18 4
17 • 3
16 6
been used as a qualitative -asure of slcin
and Congdon, 1984; Hawkms and ra
and Cooper, 19.87; Anderson and
and Tayar t al.,
a ghowed a
the solvent Pr°P;F^es°g9 > The data in this study suggests
(Anderson and Raykar, 1989). -^f °* indicator of the relative
that skinrair PC values are a better ^dicator o rmini a
skin permeability for these v°J;a^^iai g^een to identify the
permeability constants.
PBPK MODELING
chemicals into tissues. A PB-PK »odel
SSSS-
-------
Fig. 2. Comparison of skin:air partition coefficients with
octanol/water PC values.
CD
>
0
1500
1400
1300
1200
1100
1000
900
800
700
600
500
400
300
200
100
0
T 1
T r
—i—i 1—r
Xylene
-i r
Perchloroethylene
Carbon Tetrachloride
€
- Hexane
i
•
Methyl Chloroform
Methylene Chloride
Toluene
Trichloroethyle'ne
• Benzene
Dibromomethane
J '*i i '
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
Partition Coefficients
Fig. 3. Comparison of skin:air partition coefficients with
permeability constants.
E
CO
•8
-------
and elimination of a chemical are then mathematically
H«ed for each compartment which has such a process. The skin: air
described for eacn co p equation in the dermal compartment
-
iituation by extrapolating across -P--|0-nc|nSratnxtrapo?Sat?on
dermal exposure standards .
Previous work with PB-PK models in this
demonstrated their ujefulneBS xn ertrapolatjon^nd the risk
PSSS ifS"~990,f Fi.£?7rt al., 1989; Gargas et
, ppitz et al., 1988; Ramsey and Andersen, 1984).
ki-i iartitioS cSiwicients for volatile chemicals have been
^nemattcal'description that adequately describes the
biologically relevant data (McDougal, 1991).
Whole thickness skin, used in the Bkin:air J.
I,* __-, iaver which may be important in determining the
physiological compartments.
epo to benzene vapor, xposure to
cell on the dorsal skin, and exposure to
oinslan^foi
benzene
cl osed
lofr^ J^T^S 5|SeoSf sS?u Jons was one
11
-------
half the human permeability constant value used for dermal risk
assessment, 0.111 cm/h. Rat skin has been reported to be more
permeable than human skin by a factor of two to four (McDougal et
al., 1990), so the rat permeability constant was expected to be
at least twice as high as the human value for benzene.
SPECIES DIFFERENCES IN SKIN PENETRATION
INTRODUCTION
In an attempt to better understand factors affecting dermal
penetration and to be able to better extrapolate between animal
species and humans, a study was inititated to quantitate selected
anatomical differences in skin from a number of animal species.
Anatomical differences which may affect permeability include
density and size of hair follicles, density of sebaceous, and
apocri-ne glands, capillary density and distance from the surface,
as well as thickness df the various layers of the epidermis and
dermis. Animals used in the laboratory usually have fur compared
to human skin. The skin of animals provides insulation to cold
while human skin serves an evaporative cooling function. The
degree of regional variation is low in animal species while
regional variation is very high for human skin. The species and
strains to be tested for differences in skin penetration are
mouse - B6C3F1 and Hairless; rat - Fuzzy and Fischer; guinea pig
- Hartley and Hairless; Swine - farm; and Monkey - Rhesus.
Permeability constants (cm/hr) will be determined for each of
three model chemicals in species which show the widest
differences in structure. The chemicals, 1,2 dichlorobenzene,
perfluoroheptane, and chloropentafluorobenzene, were chosen on
the basis of their structural characteristics associated with'a
different class of potential toxic chemicals.
In order to understand the effect of skin structure on
permeability, anatomical and physiological differences between
the species will be correlated with the the permeability constant
for each of the three model chemicals. This analysis will
distinguish which are the important parameters that affect
permeability in skin as indicated by permeability constants.
Mathematical descriptions of the anatomical factors found to
affect skin permeability will be used to entend physiologically-
based pharmacokinetic (PBPK) models. The PB-PK models will
ultimately be used to extrapolate the toxic effects of various
chemicals between species. Once the PBPK models for the above
chemicals and species are fully developed, the models will be
validated by additional experimentation in another species.
Following model development and validation in animals, the
ability of these models to predict the consequence of human
dermal exposure to toxic chemicals will be tested by comparing
permeability data predicted by PBPK models with human data.
12
-------
METHODS
Scions of dorsal
paraffin one sat
sections was stained wltV^» SSl^t walconducted on sections
with Massons trichrome. ^^/"^f^?"" |2m (Quantimet 570C,
from each strain using an ""a|J ^Jf JSuSd were thickness
the basement membrane of the epidermis .
RESULTS
Preliminary data showed that the hairless guinea pig and farm^ig
volume .
DISCUSSION
Mditionalskin sepias are being analy^d^to C0onfir,
§44 rats and Hartley and Hairless guinea pigs .
SUMMARY
SUrf SltSf iS S^SST ff
skin and its significance in risk assessment
ACKOWLEDGEMENTS
assistance.
The animals used in this study were handled in accordance with
13
-------
the principles in the Guide for the Care and Use of Laboratory
Animals, prepared by the Committee on Care and Use of Laboratory
'Animals of the Institute of Laboratory Animals Resources,
National Research Council, DHHS, National Institute of Health
Publication #86-23, 1985, and the Animal Welfare Act of 1966, as
amended.
REFERENCES
Andersen, M.E., H.J. Clewell III, M.L. Gargas, F.A. Smith & R.H.
Reitz (1987) Physiologically Based Pharmacokinetics and the Risk
Assessment Process for Methylene Chloride. Toxicol. Appl.
Pharmacol. 87, 185-205.
Anderson, B.D. and Raykar, P.V (1989) Solute structure-
permeability relationships in human stratum corneum. J. invest.
Dermatol. 93, 280-286. •
Berner, B., Wilson, D.R., Guy, R.H., Mazzenga, G.C., Clark, F.H.,
and Maibach, H.I. (1988) The Relationship of pKa and Acute Skin
Irritation in Man. Pharmaceutical Res. 5, 660-663.
Bronaugh, R.L. and Congdon, E.R. (1984) Percutaneous absorption
of Hair dyes: Correlation with partition coefficients. J.
Invest. Dermatol. 83, 124-127.
Clewell III, H.J. and M.E. Andersen (1985) Risk Assessment
Extrapolations and Physiological Modeling. Toxicol. Ind. Health.
1, 111-131.
Clewell III, H.J. and M.E. Andersen (1989) Improving Toxicology
Testing Protocols using Computer Simulations. Toxicol. Lett. 49,
139-158.
Corley, R.A. , Mendrala, A.L., Smith, F.A., Staats, D.A., Gargas,
M.L., Conolly, R.b. , Andersen, M.E. and Rietz, R.H. (1990)
Development of a Physiologically Based Pharmacokinetic Model for
Chloroform. Tox. and Appl. Pharm. 103, 512-527.
Daniell, W., Stebbins, A., Kalman, D.,
-------
Sci. 63, 479-510.
M T ME Andersen & H.J. Clewell III (1986) A
Pharmacol. 86, 341-352.
Toxicol. Appl. Pharmacol. 98, 87-99.
"Skin" Deotation.
Hawkins, 0.8. and ^
Med. 14,
Water. JRisA: Analysis 10(4), 581-585
Pharmacol. and S^rin 1, 138-153
Hyg. Assoc. J. 48, 257-264.
, J...
j on r^i i TTT H J
McDougal, J.N. and Clewe H J"r H.J..
313-317.
l. Pharmacol. 79, 150-158
vapors in rats and humans. Fundam.
In
. 75. 378-381
Dermal to Inhalation
-to-
15
-------
McDougal, J.N., Jepson, G.W., Clewell III, H.J., MacNaughton,
M.G. and Andersen, M.E., (1986) A physiological phannacokinetic
model for dermal absorption of vapors in the rat. Toxicol. Appl.
Pharmacol. 85, 286-294,
Morgan, D.L., Cooper, S.W., Carlock, D.L.., Sykora, J.J., Sutton,
B., Mattie, D.R., and McDougal, J.N. (1991) Dermal Absorption of
Neat and Aqueous Volatile Organic Chemicals in the Fischer 344
Rat. Environ. Res. 55, 51-63.
Perkins, J.L. and Knight, V.B. (1989) Risk Assessment of Dermal
Exposure to Polychloririated Biphenyls Permeating a Polyvinyl
Chloride Glove. Am. Ind. Hyg. Assoc. J. 50, A-171-172.
Pershing, L.K., Lambert, L.D., and Knutson, K (1989) Partition
Coefficient and Solubilities of Estradiol in a Variety of
Vehicles .Predict the In Vivo Flux Across the Human Skin Sandwich
Flap. Clin. Res. 37, 727A.
Ramsey J.C. and Andersen, M.E. (1984) A Physiologically Based
Description of the Inhalation Pharmacokinetics of Styrene in Rats
and Humans. Toxicol. Appl. Pharmacol. 73, 159-175.
Reitz, H.R., J.N. McDougal, M.W. Himmelstein, R.J. Nolan, and
A.M. Schumann (1988) Physiologically-Based Pharmacokinetic Modeling with
Methylchloroform: Implications for Interspecies, High Dose/Low
Dose, and Dose Route Extrapolations. Toxicol. Appl. Pharmacol.
95, 185-199.
Sato, A. and Nakajima, T. (1979) Partition Coefficients of some
Aromatic Hydrocarbons and Ketones in Water, Blood, and Oil. Brit.
J. Indust. Med. 36, 231-234.
Scansetti, G., Piolatto, G., and Rubino, G.F. (1988) Skin
Notation in the Context of Workplace Exposure Standards. Amer. J.
Indust. Med. 14, 725-732.
Surber, C., Wilhelm, K.-P., Maibach, H.I., Hall, L.L., and Guy,
R.H. 1990) Partitioning of Chemicals into Human Stratum Corneum:
Implications for Risk Assessment Following Dermal Exposure. Fund.
Appl. Tox. 15, 99-107.
Tayar, N.E., Tsai, R.-S., Testa, B., Carrupt, P.-A., Hansch, C.,
and Leo, A. (1991) Percutaneous Penetration of Drugs: A
Quantitative Structure-Permeability Relationship Study. J.
Pharmaceutical Sci. 80, 744-749.
Tsuruta, H. (1975) Percutaneous Absorption of Organic Solvents.
1. Comparative Study of the In Vivo Percutaneous Absorption of
Chlorinated Solvents in Mice. Indust. Health 13, 227-236.
Wheeler, K.F. (1992) Barrier Lotions, Along With Gloves, Can Help
Deter Occupational Dermatitis. Occupational Health and Safety,
Jan., 60-61.
16
-------
BIOSKETCH
Thomas M. Monticello, D.V.M., Ph.D.
Dr. Monticello received his D.V.M. from Michigan State University
and following two years in clinical practice, completed a pathology
residency at North Carolina State University's College of
Veterinary Medicine, a Ph.D. in pathology from Duke University
Medical center, and a post-doctoral training program in toxicology
and experimental pathology at the Chemical Industry Institute of
Toxicology (CUT) in Research Triangle Park, NC. Dr. ^Monticello
has extensive experience in upper respiratory tract toxicology and
pathology and has authored numerous publications in this field.
Specific areas of interest include pathogenesis of chemically
induced diseases and neoplasia of the respiratory tract, cell
kinetics and carcinogenesis, and comparative medicine and
A Diplomate of the American College of Veterinary
he is currently a Senior Pathologist in the
Experimental Pathology at Bristol-Myers Squibb
pathology.
Pathologists,
Department of
U*^* KSU .&. WU1S— A A W ^^ J- *rf*fcjj^ ^^^. .»-»••—•-- —- — — — -r ~.
Pharmaceutical Research Institute in Princeton, NJ.
-------
CELL PROLIFERATION AND
FORMALDEHYDE-INDUCED RESPIRATORY CARCINOGENESIS
Thomas M. Monticello^ and Kevin T. Morgan^
1 Bristol-Myers Squibb Pharmaceutical Research Institute, Princeton, NJ, and
2CHT, Research Triangle Park, NC
Running title: Cell Proliferation and Formaldehyde Cancer
-------
Abstract
Formaldehyde is a nasal carcinogen in the rat but the cancer risk this chemical
poses for humans remains to be determined. Formaldehyde induces nonlinear,
concentration-dependent increases in nasal- epithelial cell proliferation and DNA-
protein cross-link formation following short-term exposure. Presented in this review
are results from a mechanistically-based formaldehyde inhalation study in which an
important endpoint was the measurement of cell proliferation indices in target sites for
nasal tumor induction. Male F344 rats were exposed to 0, 0.7, 2, 6, 10 or 15 PPm
formaldehyde for up to two years (6h/d, 5d/wk). Statistically significant increases in
cell proliferation were confined to the 10 and 15 ppm groups and which remained
elevated throughout the study. The concentration-dependent increases in cell
proliferation correlated strongly with the tumor response curve, supporting the
proposal that sustained increases in cell proliferation are an important component of
formaldehyde carcinogenesis. The nonlinearity observed in formaldehyde-induced
rodent nasal cancer is consistent with a high-concentration effect of regenerative cell
proliferation of the target organ coupled with the genotoxic effects of formaldehyde. Cell
kinetic data from these studies provide important information that may be utilized in the
assessment of risk for humans exposed to formaldehyde.
Key Words: rat, formaldehyde, cell proliferation, nasal cancer, risk assessment
-------
Cell Proliferation and Cancer
The complex process of cancer develops secondary to one or more mutational
events that alter growth regulatory genes of normal cells, with subsequent clonal growth
of the resulting precancerous or cancerous cells (1-2). Cell proliferation, an essential
component of the multistage process of carcinogenesis, is required for both initiation and
promotion of neoplasia in certain organs, and it plays an essential role in the later stages
of carcinogenesis, including the progression of benign lesions to malignancy and
metastasis (3-4). Each time a cell divides there is a chance, albeit rare, that a
mutational event related to the carcinogenic process will occur (6-7). Enhanced cell
proliferation may increase the frequency of these spontaneous mutations either by
errors in replication or by the conversion of endogenous or exogenous DNA adducts to
mutations before DNA repair can occur (8). Chemicals that induce cytotoxicity and
sustained increases in cell proliferation, therefore, could enhance the likelihood of
cancer development by providing additional cell divisions, each with an opportunity for
somatic or chemically-induced mutations (7, 9).
Epidemiologic evidence indicates that increased cell proliferation induced by
external or internal stimulation is a common denominator in the pathogenesis of many
human cancers. Prolonged irritation by physical or chemicals agents may cause cell
death, and the subsequent cell division that occurs during repair of the damaged tissue
may eventually lead to a cancer at the irritated site (10). For example, tobacco, an
established carcinogen, is an well known irritant. Snuff users develop leukoplakia, and
eventually, cancer of the buccal mucosa at the site of snuff application. Tobacco smoke is
a local irritant to the epithelial tissue lining the bronchi, lungs, larynx, pharynx, oral
cavity and esophagus, sites where smoking-related cancers arise.
-------
Many chemicals identified as carcinogenic for humans are genotoxic and have also
been determined to induce cancer in laboratory animals (11). The primary biological
activity of a genotoxic chemical or its metabolite, is the alteration of the genetic
information encoded in the DNA, inducing a mutation in growth regulatory genes (12).
A genotoxic chemical administered at a dose that is both cytotoxic and a cell proliferation
enhancer, would be expected to be a more effective mutagen and carcinogen then when
given at a noncytotoxic dose which does not induce cell proliferation (7-8). In addition
to regenerative cell proliferation and cytotoxicity, other cellular responses such as
metabolic activation and DNA repair could also greatly affect the carcinogenic response
of a target tissue to a given dose of a chemical (9). Understanding the relationship
between chemically induced cell proliferation and carcinogenic activity would be of value
in the investigation of mechanisms of carcinogenesis, the selection of appropriate doses
for cancer bioassays, and the improvement of risk assessment models (13).
Research with some respiratory carcinogens, such as formaldehyde gas,
illustrates the principle that both genotoxicity and enhanced cell proliferation of the
target organ should be considered in mechanistic studies and the improvement of risk
assessment models. An extensive database on cell proliferation and the induction of
upper respiratory tract tumors has been generated from a long-term, mechanistically-
based, formaldehyde bathogenesis study in the F344 rat (14). This review presents
time-course and concentration-response data on site-specific increases in nasal
epithelial cell proliferation, and compares these data with the formaldehyde-induced
tumor response. The application of these data in improving the risk assessment process
will also be addressed.
-------
Formaldehyde
Formaldehyde is an important commodity chemical used widely in the
manufacture of resins, particle board, plywood, textiles, and many other consumer
products. The finding that formaldehyde is a nasal carcinogen in rats (15-17) has
provoked concern that this chemical may also pose a cancer risk for humans. Although
there is widespread, exposure of humans to formaldehyde, epidemiological data for
exposed individuals are equivocal with respect to any causal association between
formaldehyde exposure and nasal cancer incidence (18-21). This finding has stimulated
a series of research projects aimed at understanding the mechanisms involved in
formaldehyde-induced toxicity and carcinogenesis.
' The genotoxic effects of formaldehyde have been extensively reviewed (22).
Formaldehyde induces gene mutations in many organisms including bacteria, fungi,
yeast, fruit flies, and in cultured mammalian cells. Formaldehyde has also been shown
to induce single-strand DNA breaks, sister chromatid exchanges, and chromosomal
aberrations in a variety of cultured mammalian cells, including human bronchial cells
(23).
Formaldehyde induces a variety of toxic effects in experimental animals. It is a
=*
potent upper respiratory irritant and cytotoxicant that is almost entirely deposited in
the anterior nasal cavity of rodents (24). Metabolized in the nasal mucosa,
formaldehyde reacts covalently with DNA, RNA and proteins. The covalent reactions of
formaldehyde with macromolecules are generally accepted as the fundamental causes of
its toxic effects (24).
-------
In, the chronic formaldehyde bioassay (17), the relationship between the
incidence of nasal tumors in rats and the concentration of formaldehyde was distinctly
nonlinear. At 2 ppm, no nasal tumors were present, while between 5.6 and 14.3 ppm,
the tumor incidence increased 50-fold as exposure concentration rose less than 3-fold.
Other concentration-dependent responses in rats exposed to formaldehyde include
inhibition of mucociliary function, cytotoxicity, inflammation, and induction of DNA-
protein cross-links (24-25).
Molecular dosimetry studies in rats exposed to a range of formaldehyde
concentrations using a DNA-protein cross-linking assay, have shown that formaldehyde
induces cross-links in rat nasal respiratory mucosa following exposure to > 2 ppm
formaldehyde (26). The rate of formation of these cross-links is a nonlinear function of
the airborne formaldehyde concentration, increasing more rapidly at high than at low
concentrations. The yield of cross-links at a given exposure concentration is probably
determined by the ability of host defense mechanisms, such as metabolism and DMA
repair, to maintain the integrity of the DNA. Companion studies by Casanova and Heck
d'A (27), investigating glutathione mediated metabolism of formaldehyde, have shown
that saturable metabolism of formaldehyde is an important defense mechanism against
the formation of cross-links. Currently, DNA-protein cross-links are used as a
measure of formaldehyde dose at the site of tumor formation (28).
Formaldehyde-Induced Cell Proliferation
Increased nasal epithelial cell proliferation, another important biological
response of the rat exposed to formaldehyde, is a sensitive indicator of respiratory
epithelial cell toxicity (29). Increased cell proliferation in response to formaldehyde
exposure has also been demonstrated in nonhuman primates (30) and xenotransplanted
-------
human nasal respiratory epithelium (31). Little is known, however, about the
mechanisms of these proliferative responses, which may involve autocrine and
paracrine growth factors, mutation in growth regulatory genes, and/or regenerative
stimuli brought about by death of adjacent cells (6, 32-34)
Epithelial injury with consequent hyperplasia is a common feature of many
chemically-induced toxic responses, including those induced by formaldehyde gas.
Following cytotoxic insult, epithelium of the upper respiratory tract, including the
nasal passages, may undergo a dynamic series of alterations to include hyperplasia,
metaplasia, dysplasia, carcinoma in situ or intraepithelial neoplasia, and carcinoma
(35). These alterations are all characterized by a common feature, increased cell
proliferation (35).
Recent studies have demonstrated that formaldehyde-induced lesions and
increases in cell proliferation in rats following acute (1, 4, 9, days or 6 weeks), or
subchronic (3 month) exposure were concentration-dependent. Nasal epithelial lesions
occurred in specific regions of the anterior nasal passages, primarily the walls of the
lateral meatus, mid-septum, and medial aspect of the maxilloturbinate (29, 36). The
increases in nasal cell proliferation were associated with formaldehyde-induced nasal
lesions which included epithelial degeneration, necrosis, hyperplasia, and squamous
metaplasia.
Increased nasal cell proliferation was present in rats exposed to 6, 10, or 15
ppm formaldehyde following up to 6 weeks of exposure, while no increases were detected
in the 0.7 or 2 ppm groups (29). After 3 months exposure, increases in cell
proliferation were confined to only the 10 and 15 ppm groups (14). These short-term
studies demonstrated that 0.7 and 2 ppm formaldehyde do not induce increases in nasal
-------
cell proliferation and that 6 ppm formaldehyde induces transient increases in cell
proliferation that return to control levels by 3 months. The transient increase in cell
proliferation observed at 6 ppm emphasizes the importance of evaluating multiple time
points during a cell proliferation study. Time-course data on site-specific increases in
cell proliferation provide important information which is necessary for certain
biologically-based risk assessment models of formaldehyde carcinogenesis (37).
Various metaplastic-dysplastic or preneoplastic lesions occur in the respiratory
epithelium of both laboratory animals and humans following exposure to carcinogens
(38-39). Preneoplasia is generally believed to be a precursor response that has a high
probability of developing into neoplasia. Formaldehyde exposure induces putative
preneoplastic lesions in rat nasal epithelium following exposure to carcinogenic
concentrations (15 ppm) for several months (40-41). The preneoplastic lesions were
characterized by epithelial hyperplasia-metaplasia with atypia, similar to those
reported in extranasal respiratory epithelium (38).
Cell proliferation rates in formaldehyde-induced preneoplastic lesions were
significantly higher than those of control nasal epithelium (40). Labeled cells were
present throughout the lesion, including the superficial layers, suggesting either a rapid
migration from the basal cell population to the surface, or the capability of cells
throughout the lesion to undergo replicative DMA synthesis (40). Data on
formaldehyde-induced putative preneoplastic lesions, such as number of cells per
lesion, cell proliferation rate per lesion, and other parameters, can be utilized in
multistage models of carcinogenesis, such as the Moolgavkar-Venzon-Knudson model
(37).
8
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The tumor incidence from the long-term formaldehyde cell proliferation study
(14) confirmed the previously reported bioassay tumor response (17). Similar to the
bioassay (17), the majority of tumors were squarnous cell carcinomas and arose from
the nasal epithelium lining the walls of the anterior lateral meatus and the nasal septum
(14), the same locations reported in other chronic formaldehyde studies (42-43) and
the sites evaluated for alterations in cell proliferation. Nonneoplastic nasal lesions
following long-term exposure were concentration-dependent, and included
inflammation, epithelial hyperplasia, squarnous metaplasia, and necrosis with
exfoliation.
There were no detected treatment-induced responses in cell proliferation in the
three lowest formaldehyde concentration groups, 0.7, 2, and 6 ppm, following exposure
for up to 18 months. Increases in cell proliferation were present only in the 10 and 15
ppm groups, and were generally greater in the 1 5 ppm group as compared to 10 ppm
(14). At each time point evaluated (3, 6, 12, and 18 months), a good correlation
existed between the concentration-dependent increases in cell proliferation and the
tumor incidence, supporting the proposal that sustained increases in cell proliferation
are an important component of formaldehyde carcinogenesis (14).
Site-specific increases in cell proliferation following long-term exposure to
formaldehyde were not only present at the lateral meatus and nasal septal locations, but
also the medial maxilloturbinate (MMT) site, even though the number of tumors
originating from this site was disproportionately lower as compared to the other sites at
risk. This discrepancy may be attributed to a decreased probability of cell mutation and
subsequent cancer, due to the significantly smaller MMT target area and cell population
at risk. Site specific nasal responses could also be due to differences in regional
susceptibility to formaldehyde, or other, as yet unidentified, factors.
-------
The association of epithelial cytotoxicity, cell proliferation and nasal cancer has
also been demonstrated in a study where male rats with damaged or undamaged nasal
mucosa were exposed to 10 PPm formaldehyde (44). The nasal damage was induced by
bilateral intranasal electrocoagulation of the anterior third of the nasal passages. Rats
with damaged nasal mucosa exhibited increases in formaldehyde-induced rhinitis,
hyperplasia, and metaplasia of the nasal epithelium. Exposure to 10 ppm formaldehyde
for 28 months produced an 8-fold increase in nasal squamous cell carcinomas in rats
with damaged noses then in those with intact noses (i.e, not pretreated with nasal
electrocautery but similarly exposed). These researchers concluded that both severe
damage to the nasal mucosa and hyperoroliferation are important in the development of
nasal tumors in rats exposed to formaldehyde.
Cell Proliferation and Formaldehyde Risk Assessment
Cell death and renewal are predominant features of most toxicologic injuries to
.the respiratory epithelium. Toxicant-induced cell necrosis, followed by regeneration,
could, therefore, be a major determinant in chemically-induced respiratory tract
carcinogenesis. The studies of cell proliferation and nasal epithelium in formaldehyde-
exposed rats demonstrate a good correlation of cellular injury and cell proliferation.
The proliferate and tumor response are dependent on formaldehyde concentration.
Induction of nasal carcinoma in rats by formaldehyde requires long-term.exposure to
' high concentrations that result in cell death, followed by regenerative hyperplasiajand
metaplasia, changes associated with increases in cell proliferation. Since cell
proliferation is clearly involved in chemical carcinogenesis, these concentration-
responsive changes represent potentially important data that could be included in the
risk assessment process.
10
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A pharmacokinetic model, utilizing the rate of formaldehyde-induced DNA
protein cross-link formation, has been described in which the concentration of
formaldehyde-induced cross-links formed in corresponding tissues of different species
can be predicted by scaling certain parameters (45). DNA protein-cross links are not
the only biological factor affected by formaldehyde exposure, however, and a
biologically-based risk assessment strategy for inhaled formaldehdye has been proposed
(37). The biologically-based model incorporates molecular dosimetry and cell kinetic
data, since both of these factors are causally involved in formaldehyde-induced rodent
nasal carcinogenesis (37).
Over the past decade, mechanistic studies have provided a great deal of
information on the pathogenesis of formaldehyde-induced nasal carcinogenesis. This data
is important for the assessment of human risks at formaldehyde concentrations below
those at which cancer develops in rodent bioassays. Quantitative risk assessment
methods should improve our. understanding of the shape of the formaldehyde nasal cancer
exposure-response curve, and the quantitative importance of cell proliferation and
mutation in this process. Moreover, the incorporation of mechanistically based data will
improve low-dose, and interspecies extrapolation of formaldehyde risk.
11
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References
1. Pitot HC. Fundamentals of Oncology, 3rd ed. New York, Marcel Dekker Publ., 1986.
2. Levine AJ, Momand J, Finlay CA. The P53 tumor suppresser gene. Nature
351:453-456,1991.
3. Grisham JW, Kaufmann WK, 'Kaufman DG. The cell cycle and chemical
carcinogenesis. Surv Syn Pathol Res 1:49-66, 1983.
4. Farber E, Sarma DRS. Hepatocarcinogenesis: A dynamic cellular perspective. Lab
Invest 56:4-22, 1987.
5. Russo J, Russo IH. Biological and molecular bases of mammary carcinogenesis. Lab
Invest 112-137, 1987.
6. Loeb LA. Endogenous carcinogenesis: Molecular oncology into the twenth-first
century. Cancer Res 49:5489-5496, 1989.
7. Ames BN, Gold LS. Too many rodent carcinogens: mitogenesis increases
mutagenesis. Science 249, 970-971, 1990.
8. Butterworth BE, Goldsworthy TL. The role of cell proliferation in multistage
carcinogenesis. Proc Soc Exper Biol Med 198:683-687, 1991.
9. Cohen SM, Ellwein LB. Genetic errors, cell proliferation, and carcinogenesis.
Cancer Res 51:6493-6505, 1991.
10. Preston-Martin S, Pike MC, Ross RK, Henderson BE. Cell division and human cancer.
Prog Clin Biol Res 369:21-34, 1991.
11. Chemicals and industrial processes associated with cancer in humans. IARC
Monographs, Suppl 1. Lyon, France: International Agency for Research on Cancer,
1979.
12
-------
1 2. Butterworth, BE. Consideration of both genotoxic and nongenotoxic mechanisms in
predicting carcinogenic potential. Mut Res 239:117-1 32, 1990.
13. Eldridge SR, Tilbury LF, Goldsworthy TL, Butterworth BE. Measurement of
chemically induced cell proliferation in rodent liver and kidney: a comparison of
5-bromo-2'-deoxyuridine and [3-H]thymidine administered by injection or
osmotic pump. Carcinogenesis 11:2245-2251, 1990.
14. Monticello TM. Formaldehyde-induced pathology and cell proliferation. PhD
9-
Dissertation, Duke University, Durham, NC, 1990.
15. Swenberg JA, Kerns VVD,.Mitchell Rl, Gralla EJ, Pavkov KL: Induction of squamous
cell carcinomas of the rat nasal cavity by inhalation exposure to formaldehyde
vapor. Cancer Res 40:3398-3402, 1980.
1 6. Albert RE, Sellakumar AR, Lashkin S, et. al. Gaseous formaldehyde and hydrogen
chloride induction of nasal cancer in the rat. J Natl Cancer Inst 68:597-603,
1982.
1 7. Kerns WD, Pavkov KL, Donofrio DJ, Gralla EJ, Swenberg JA. Carcinogenicity of
formaldehyde in rats and mice after long-term inhalation exposure. Cancer Res
43:4382-4392, 1983.
1 8. EPA. Formaldehyde risk assessment update, final Draft. Office of Toxic Substances,
US Environmental Protection Agency, Washington, DC, 1991.
19. Universities Associated for Research and Education in Pathology, Inc. Epidemiology
of chronic occupational exposure to formaldehyde: Report of the ad hoc panel on
health aspects of formaldehyde. Toxicol. Ind. Health 4, 77-90, 1988.
20. Starr TB, Gibson JE, Barrow CS et al. Estimating human cancer risk from
formaldehyde: Critical issues. IN "\f. Turoski (ed)., Formaldehyde: Analytical
Chemistry and Toxicology, Advances in Chemistry Series No. 210, pp 300-333.
Washington DC: American Chemical Society, 1985.
13
-------
21. ' Blair A, Stewart M, O'berg W, et al. Mortality among industrial workers exposed to
formaldehyde. J Natl Cancer Inst 76:1071-1084, 1986.
22. Ma TH, Harris MM. Review of the genotoxicity of formaldehyde. Mutation Res
196:37-59, 1988.
23. Grafstrom RC, Fornace A, Autrup H, Lechner JF, Harris CC. Formaldehyde damage
to DMA and inhibition of DMA repair in human bronchial cells. Science
220:216:218,1983.
24. Heck H'dA, Casanova M, Starr TB. Formaldehyde toxicity - New understanding.
Critical Rev Toxicol 20:397-426, 1990.
25. Swenberg JA, Barrow CS, Boreiko CJ, et. al. Nonlinear biological responses to
formaldehyde and their implications for carcinogenic, risk assessment.
Carcinogenesis 4:945-952, 1983.
26. Casanova M, Deyo DF, Heck Hd'A. Covalent binding of inhaled formaldehyde to DNA
in nasal mucosa of F344 rats:analysis of formaldehyde and DNA by high-
performance liquid chromatography and provisional pharmacokinetic
interpretation. Fundam Appl Pharmacol 12:397-417, 1989.
'27. Casanova M, Heck Hd'A. Further studies on the metabolic incorporation and
covalent binding of inhaled [3H]- and [14C]formaldehyde in F344 rats:effects of
glutathione depletion. Toxicol Appl Pharmacol 89:105-1 21,1987.
28. Environmental Protection Agency. Formaldehyde Risk Assessment Update, June 11,
1991. Washington, DC: Office of Toxic Substances, US EPA, 1991.
29. Monticello TM, Miller FJ, Morgan KT. Regional increases in rat nasal epithelial
cell proliferation following acute and subchronic inhalation of formaldehyde.
Toxicol Appl Pharmacol 111:409-421, 1991.
30. Monticello TM, Morgan KT, Everitt Jl, Popp JA. Effects of formaldehyde gas on the
respiratory tract of rhesus monkeys. Am J Pathol 134:515-527, 1989.
14
-------
31. Klein Szanto AJP, Ura H, Resau J. Formaldehyde-induced lesion of
xenotransplanted nasal respiratory epithelium. Toxicol Pathol 17:33-37 (1989).
32. Pardee AB. Biochemical and molecular events regulating cell proliferation. J
Pathol 149: 1-2, 198(5.
33. Lutz WK, Maier P. Genotoxic and epigenetic chemical carcinogenesis: one process,
different mechanisms. Trends in Pharmacol Sci 9:322-326, 1988.
34. Recio L, Sisk S, Pluta L, et. al. p53 mutations in formaldehyde-induced nasal
squamous cell carcinomas in rats. Cancer Res 52:611 3-611 6, 1992.
35. Klein-Szanto AJP. The role of chemically induced epithelial hyperplasia in the
development of human cancer. Prog Clin Biol Res 369:35-41, 1991.
36. Morgan KT, Kimbell JS, Monticello TM, Patra AL, Fleishman A. Studies of
inspiratory airflow patters in the nasal passages of the F344 rat and rhesus
monkey using nasal molds: Relevance to formaldehyde toxicity. Toxicol Appl
Pharmacol 110:223-240, 1991.
37. Conolly RB, Monticello TM, Morgan KT, Clewell HJ, Anderson ME. A biologically-
based risk assessment strategy for inhaled formaldehyde. Comments Toxicol 4:269-
293, 1992.
38. Nettesheim P, Klein-Szanto AJP, Marchok AC et. al. Studies of neoplastic
development in respiratory tract epithelium. Arch Pathol Lab Med 105:1-10,
1981.
39, Klein-Szanto AJP, Topping DC, Heckman CA, Nettesheim -P. Ultrastructural
characteristics of carcinogen-induced dysplastic changes in tracheal epithelium.
Am J Pathol 98:83-100, 1980.
40. Monticello TM, Morgan KT. Cell kinetics and characterization of "preneoplastic"
lesions in nasal respiratory epithelium of rats exposed to formaldehyde. Proc
Amer Assoc Cancer Res, 30:195, 1989.
15
-------
41. ' Morgan KT, Monticello TM. Formaldehyde toxicity: Respiratory epithelial injury
and repair. IN Biology, Toxicology and Carcinogenesis of Respiratory Epithelium,
DG Thomassen and P Nettesheim, eds. pp. 155-171, Hemisphere Publ, New York,
1990.
42 Morgan KT, Jiang XZ, Starr TB, Kerns WD. More precise localization of nasal tumors
associated with chronic exposure of F344 rats to formaldehyde gas. Toxicol Appl
Pharmacol 82:264-271, 1986.
43 Woutersen RA, Feron VJ. Localization of nasal tumors in rats exposed to acetaldehyde
or formaldehyde. IN: Nasal carcinogenesis in rodents: relevance to human health risk
(VJ Feron and MC Bosland, eds.), Pudoc, Wageningen, 1989.
44 Woutersen RA, van Gardeen-Hoetmet A, Bruijnjes JP, Zwart A, Feron VJ. Nasal
tumors in rats after severe injury to the nasal mucosa and prolonged exposure to 10
ppm formaldehyde. J Appl Toxicol 9:39-46, 1989.
45 Casanova M, Morgan KT, Steinhagen WH et. al. Covalent binding of inhaled
formaldehyde to DNA in the respiratory tract of rhesus monkeys: Pharmacok.net.es,
rat-to-monkey interspecies scaling, and extrapolation to man. Fund Appl Toxicol
17:409-428, 1991.
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BIOGRAPHY
James E. Trosko, Ph.D.,
Department of Pediatrics/Human Development
College of Human Medicine
Institute of Environmental Toxicology
Michigan State University
East Lansing, Michigan.
Having received his Ph.D. from Michigan State University in 1963 in
the area of radiation genetics, Dr. Trosko spent three years as a
postdoctoral fellow at Oak Ridge National Laboratory, working under
Drs-. Sheldon Wolff, Ernest Chu and Richard Setlow in the areas of
radiation-induced DNA damage/repair and -mammalian mutagenesis
After initiating an academic career at Michigan State University in
1966, he became a NCI-Career Development Awardee (1972-1977) . This
carcinogenesis at the McArdle Lab for Cancer Research at the
University of Wisconsin under Dr. Van R. Potter. He rose throuqh
the academic ranks at MSU to Full Professor, and along the way was
awarded the Teacher-Scholar Award, the Distinguished Faculty Award
Sigma Xi Senior Scientist Award, and Outstanding Educator of
America Award. Most recently he was given the opportunity to serve
as Chief of Research at the Radiation Effects Research Foundation
in Hiroshima, Japan (1990-1992). He is the author or co-author of
over 200 journal and book articles.-
_
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PHE ROLE OF MODULATED GAP JUNCTIONAL INTERCELLULAR
COMMUNICATION IN EPIGENETIC TOXICOLOGY
Abbreviated title: GAP JUNCTIONS IN EPIGENETIC TOXICOLOGY
JAMES E. TROSKO, PH.D.1'3
CHIA-CHENG CHANG, PH.D1
BURRA V. MADHUKAR, PH.D.2
Department of Pediatrics and Human Development
College of Human Development
Institute of Environmental Toxicology
Michigan State University
East Lansing, Michigan 48824
'Department of Pharmacology and Toxicology
Indiana Medical School
Indianapolis, Indiana 46202
Corresponding author:
Department of Pediatrics and Human Development
College of Human Development
Institute of Environmental Toxicology
Michigan State University
East Lansing, Michigan 48824
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ABSTRACT
The normal development and health of all multi-cellular
organisms, including the human being, depend on the adaptive
maintenance of the integrity of the genetic information (e.g.,
DNA protective and repair mechanisms), as well as of the homeo-
static and cybernetic regulatory systems within and between
tissues. The primary focus of the past and current toxicological
studies and risk assessment practices has been to ascertain and
predict the "genotoxicity" of various physical and chemical agents.
The paradigm of "carcinogen as mutagen," while valuable for
stimulating studies of the detection of mutagens and of their
potential role in "causing" somatic and germ line diseases, has
tended to blunt research on the role of non-genotoxic mechanisms
in disease causation.
This brief analysis will emphasize the need to consider the
role of modulated gap junctional intercellular communication (GJIC)
in any biological risk assessment mode. It is based on the
following assumptions and facts. Since gap junctions exist in all
metazoans", they have been associated with the regulation of cell
proliferation, development, differentiation and the adaptive
function of both excitable and non-excitable coupled cells. A
family of highly evolutionarily conserved genes codes for proteins
(connexins), which, as hexameric units (connexons), form membrane
associated channels. Cells coupled by gap junctions will have
their ions and small regulatory molecules equilibrated. Regulation
of GJIC can be at the transcriptional, translational or post-
translational levels. Transient down or up regulation of GJIC can
2
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be induced by endogenous or exogenous chemicals via many mechanisms
at any of these three levels. Stable abnormal regulation has been
associated with activated oncogenes, and normal regulation has been
associated with several tumor suppressor genes.
The dysfunction of these gap junctions might play a role in
the actions of various toxic chemicals which have cell
type/tissue/organ specificity. This could bring about distinct
clinical consequences, such as embryo lethality or teratogenesis,
reproductive dysfunction in the gonads, neurotoxicity of the CNS,
hyperplasia of the skin, and tumor promotion of initiated tissue.
Modulation of GJIC should be viewed as a scientific basis of
•epigenetic toxicology' since the alteration of intercellular
communication would alter the internal physiological state of the
cell. The inhibition of GJIC is a necessary component of
mitogenesis (a necessary component of the multi-stage carcinogenic
process). The modulation of GJIC can have both toxicological, as
well as therapeutic potential.
KEY WORDS: Gap junctions; epigenetic tocicology; intercellular
communication; connexins; oncogenes
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•During evolution, long-lived multi-cellular organisms must have
developed defense mechanisms to protect them against the
carcinogenic and other deleterious effects of spontaneous
mutations Protagonists of the theory of cell replication leads
to cancer do not deal with, this aspect or explain how this barrier
might be broken during tumor development.'
I.E. Weinstein (1)
INTRODUCTION; BIOLOGICAL BASTS POR RISK ASSESSMENT: WHAT FACTORS
ARE.... INVOLVED?
Implicit in the acute and chronic exposure to radiations and
chemicals is a risk to normal short and long-term functioning of a
multi-cellular organism and to its offspring. Predictions of the
potential "toxicities- are being made on the bases of mathematical
models based on incomplete empirical or epidemiological data, known
or suspected mechanisms of action of the agent on a single level of
biological organization and extrapolations from a variety of
laboratory animal studies (2) . In lieu of complete understanding of
how these toxic agents might act in the human being (which will
never be possible), we are left with the challenge to develop risk
assessment models that, at least, begin to approach having a
biological basis (3).
The quotation of I.E. Weinstein serves to illustrate the point
that, as toxicologists, we have a long way to go. On one end of
the problem, we must recognize that a multi-cellular organism, such
as the human being, is not just a collection of 10" independent
cells in a hairy-covered bag made of skin. We are the, result of
the hierarchical process (4) of cybernetically interacting elements
4
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(5) . Fundamentally, the health and adaptive ability (homeostasis)
of a multi-cellular organism is based on a number of systems on the
molecular, biochemical, cellular, tissue, organ, system
(physiological, immune, nervous) levels (4). Biological organisms,
both single and multi-cellular, have developed a series of adaptive
mechanisms at each of these levels to survive the constant
exposure, either acute or chronic, of radiations and chemicals. The
toxic endpoint at the cellular level could be mutation of the
genome, cell death or epigenetic alteration of the phenotype (5)
[Figure 1].
GENOTOXICANT
EPIGENETIC TOXICANT
CHROMOSOMAL
MUTATION
©
TRANSCRIPTIONAL
MODIFICATION
TRANSLATIONAL
MODIFICATION
POSTTRANSLATIONAL
MODIFICATION
Fig. 1 Diagram illustrating the difference between
gXotoxic and epigenetic chemicals. Those ^at alter the
Duality or quantity of genetic information are
while those that affect the expression of the
information, at the transcriptlonal, W"81*
posttranslational levels are epagrenetic
(Reprinted from Trosko et al, In: In Vitro
Toxicology, G. Jolles and A. Cordier, eds, Academic
Press, NY, 1992; used with permission)
-------
The presence of melanin in the skin . tissue or drug-
metabolizing enzymes in the cell could protect the DNA fronr-
ultraviolet light or chemical induced damage, respectively. DNA
repair enzymes have the potential to either restore the genome to
its original condition, or, if not repaired or repaired in an error-
prone fashion, can lead to a viable or non-viable mutation.
Depending of the nature of the mutation, the gene mutated, whether
the mutated gene is expressed, whether the mutated cell is
replicated, the mutation might have little biological significance
because of cellular redundancy or it might have devastating somatic
or hereditary consequences (6) . At the tissue level, cell death,
depending on the number of cells, which cells die, at what stage of
development, might or might not affect the homeostatic or adaptive
status of the multi-cellular organism. For example, the death of
one heptacyte might not even elicit a regenerative' response of the
liver. On the other hand, the death of the single stem or
progenitor cell of a critical organ during development could have
lethal or devastating teratogenic effects. The death of many cells
could induce compensatory or regenerative hyperplasia, which,
depending on the cause of the death [apoptosis (7,8) or induced
cytotoxicity], lead to natural tissue restructuring, wound healing,
scar tissue formation or tumor promotion (2). At the organ and
system levels, control of both cell growth and differentiation is
mediated by extracellular communication mechanisms [immune systems,
neuro-endocrine systems] (9) . In other words, even if a viable
mutation occurs, in a single cell, if tissue, organ and system
suppression mechanisms prevent the product of that abnormal cell to
-------
disrupt homeostasis or prevent the mutated cell to clonally
increase, then these systems act as another protective barrier to
maintain health and homeostasis.
While the details of each of these barriers at the molecular,
biochemical, cellular and physiological levels have been recognized
and studied to various degrees/one seems to have been largely
ignored in the field of toxicology and risk assessment. How the
multi-cellular organism suppresses the potential toxicological
consequences of abnormal cell proliferation/differentiation of
either normal or mutated cells will be the topic of this analysis.
Specifically, the role of intercellular communication will be
examined in the context of acting as a barrier to cell
proliferation and how the modulation of intercellular communication
could contribute to the disruption of homeostasis. This new concept
will form the basis of the emerging field of -epigenetic
toxicology' (10,11).
Current Problems in Risk Assessment
Fundamentally, extrapolations from data derived from in vitro
and in vivo tests, as well as from human epidemiological data, are
dependent on both the theories of the disease endpoints in which
one is interested, the limitations of each test system (assuming
for the moment the validity of the design and execution of the
experiment), and the assumptions of the extrapolations from the
non-human test data to the human population/individual. Limitations
of in vitro tests, especially those designed to detect genotoxic
chemicals (12-14), and of in vivo bioassays (15-22) have been
noted. Epidemiological approaches are characterized by poor
7
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sensitivity and the inability to determine underlying mechanisms.
Compounding the problem are the facts of interactions of physical
and chemical exogenous agents with endogenous factors having
genetic, developmental and sex elements (23).
Recently the new concept of 'molecular epidemiology has
emerged (24-26) . It is based on the assumption that if a given
causative agent leaves a unique molecular fingerprint in the
diseased tissues/cells/macromolecules/DNA, then one might predict
and prevent these diseases (26) . While there exists some promise
to this new approach, some of the same problems will plague this
approach since it is based on some of the same assumptions and
limitations of the previous approaches.
In order to limit this discussion, the endpoint of cancer will
be used to illustrate the objective of this analysis. However, it
should be obvious from this analysis that other disease" endpoints,
such as teratogenesis, reproductive dysfunction, immune
dysfunction, neurotoxicity, cardiovascular diseases and other
disease states, will be involved.
Mutagenesis and Mitooenesis in Carcinoqenesis
It is in the underlying assumption of the mechanism of
carcinogenesis^ from which much of the problems of risk assessment
to cancer after exposure to radiation or chemicals come. It is now
abundantly clear that no one thing "causes" cancer ,(27) . Pathology
information and epidemiological data show that human cancer is the
result of multiple steps during the evolution of a normal cell to
a metastatic cell (28-30) . While the role to mutations in cancer
was well known to geneticists via the various hereditary
8
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predispositions to cancer (xeroderma pigmentosum, Downs,
retinoblastoma, Wilms, Fanconi's, etc.), it took some time before
a more general acceptance of the somatic mutation theory of cancer
became commonly accepted. The introduction of the concept,
-carcinogens as mutagens- (31), helped to spur the development of
new interests, assays, and experiments into the detection of
"carcinogens'. Identification of DNA lesions, such as pyrimidine
dimers, being correlated with higher mutations and cancers in cells
of sun-light induced skin cancer-prone syndromes, xeroderma
pigmentosum (32-35), together with the identification of
electrophilic-induced DNA lesions in tissues capable of
metabolizing chemicals (36), helped to shape the idea that
mutations were responsible for cancer.
With the relatively recent identification of -oncogenes-
(37,38), and more recently the -tumor suppressor- genes (39,40),
more evidence was supplied to bolster the somatic mutation theory
of cancer. With modern molecular technology, it became clear that
these important oncogenes and tumor suppressor genes in tumor cells
were often mutated. However, as with most exciting new scientific
theories, the. beginning euphoria starts to wear thin and the new
paradigm must address challenges. These challenges included the
animal experiments showing the multi-step nature of.carcinogenesis
(41), being conceptualized by the -initiation-, -promotion", and
-progression- stages. In addition, many of the so-called
•carcinogens- in the animal bioassay test were shown to be non-
mutagens in various in vitro assays presumably designed to detect
mutagens (42). Furthermore, most of these animal tumor promoters
-------
were shown also to be non-mutagenic in these in vitro genotoxicity
assays (12,13) .
Recently, the idea emerged that since initiation of the
carcinogenic process started in a single cell [evidenced by the
irreversibility of the event in a single cell and the monoclonal
nature of most tumors (43,44)], the ultimate appearance of a multi-
celled tumor from this single initiated cell most have been the
result of the promotion process (45) . In other words, promotion
must be, at least, a mitogenic process for the initiated cell (11) .
Since many tumor promoters and promoting conditions (i.e., surgery,
cell killing (46-48)] seemed to be associated with hyperplasia and
cell proliferation, it seemed plausible to assume that mitogenesis
is a necessary component of carcinogenesis (49) . The idea was
further supported by the findings that many tumor promoters acted
as non-genotoxic agents in several in vitro tests for genotoxicity
(50) and many were known to be mitogenic in either in vitro or in
vivo systems, such as growth factors and hormones. In addition, if
multiple genetic 'hits' were needed for the carcinogenic process to
occur, mitogenesis was necessary to complete the process since, by
definition, a mutation is the hereditary transmission of an
alteration of a change in the genome. Both spontaneous and induced
mutations depend on mitogenesis to 'fix" the alteration in DNA.
However, as correctly pointed out by Weinstein (1), excessive cell
proliferation, in and of itself, is probably not the causative
factor in most cancers. The observations that additional exposures
to initiating agents are necessary to convert promoted benign
tumors to carcinomas also supports the notion that, while
10
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mitogenesis is a necessary component of carcinogenesis (51-57), it
is an insufficient causative agent (58).
Gao Junctional Communication in Mitoaenesis and Tumor Promotion
In order to explore the role of mitogenesis in carcinogenesis,
particularly as to how the tumor promotion concept relates to it,
the potential role of gap Junctional communication in mitogenesis
will be examined. The concept of contact inhibition (59) was
created to explain the fact that most normal cells stop
proliferating, in vitro, when they come in direct contact with each
other. Cancer cells have been characterized by their inability to
contact inhibit (60,61). With the demonstration that most, if not
all, cancer cells exhibit some form of dysfunctional GJIC (62-65),
it seemed logical to link GJIC with the control of cell
proliferation. In addition, many known endogenous and exogenous
tumor promoting chemicals have been shown to reversibly down
regulate GJIC, either at the transcriptional, translational or
posttranslational levels (50,66). Even physical tumor promoting
conditions, such as partial hepatectomy (67), have been associated
with the down regulation of GJIC during the regenerative phase
(68). "cell killing, which does release extracellular mitogenic
stimulating chemicals needed for regeneration, could indirectly
cause the down regulation of GJIC and bring about a tumor promoting
effect. Prostaglandins and their metabolites have been associated
with the lysis of dead cells and the down regulation of GJIC
(69-72). Genotoxic agents, depending on the dose or concentration
(73), could be either initiators or -complete carcinogens'. At non-
cytotoxic levels, these agents might primarily initiate. At
11
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cytotoxic levels, they would initiate as well as promote the
surviving initiated cell. There are also, non-genotoxic chemicals,
such as alcohol or heptocytotoxic viruses, which could kill cells,
thereby forcing compensatory hyperplasia of any surviving
spontaneous or induced initiated cell. Many alcohols have been
4
shown to inhibit GJIC at near or at cytotoxic levels (74).
While the evidence rigorously supporting the role of GJIC in
"contact inhibition" and growth control has not yet been generated,
there is strong inferential support for this hypothesis. Few, if
any, cells which are gap junctionally coupled have been
demonstrated to be dividing. In addition, strong correlations with
the lack of GJIC have been shown with cells which are not gap
junctionally coupled (see 58). Also, as previously noted, tumor
promoting chemicals, growth factors, or physical conditions, such
as cell removal or killing, have been associated with both
mitogenesis of the initiated cell and reduction of GJIC (50). The
observations that some mitogens or mitogenic conditions are not
promoters (75,76), is, in and of themselves, not rigorous proof
that GJIC is not involved. Unless it can be shown that the
mitogenic .stimuli is both sustained (77) and is influencing the
initiated cell, mitogenic stimuli which are only transient and
affecting only the non-initiated cell will not be a promoter. In
addition, any mitogenic assay which indicated that a given chemical
did not act as a mitogen in a given tissue/ organ does not prove
the chemical might not be a tumor promoter since it might not act
as a mitogen for normal cells but only for the few initiated cells
in the tissue. The observation that phenobarbital can be a promoter
12
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in the liver is not determined by the observation that there exists
a sustained hyperplasia in the liver, but the selective clonal
expansion of the initiated cells. The fact that "apoptosis' is
inhibited by several agents that are tumor promoters raises an
interesting concept that the selective accumulation of initiated
*
cells may well be the end result of the blockage of GJIC needed for
controlled or programmed cell death (78) . In other words, in
closed organs, such as the liver, apoptosis is a necessary means to
control the volume of the organ. A balance between cell birth and
cell death might be necessary to maintain the stable number of
cells (79) . The inhibitions of GJIC could affect either or both the
control of cell division or cell death.
Another line of evidence linking mitogenesis with GJIC comes
from the field of oncogenes and tumor suppressor genes. Proto-
oncogenes and their activated counterparts, oncogenes, are defined
as those genes controlling cell proliferation and differentiation
(37,38). On the other hand, tumor suppressor genes are those
normal genes which, by definition, prevent cell proliferation
(39,40). A number of oncogenes have now been associated with the
down-regulation of GJIC and cell proliferation (80-92) .
Furthermore, several tumor suppressor genes have been associated
with the up-regulation of GJIC (93,94).
In recent years, several anti-tumor promoters or anti-
carcinogens have also been associated with the up-regulation of
GJIC. Retinoids (95-99), c-AMP (100-103), carotenoids (104) and
lovastatin (105) have been linked with increased GJIC and decreased
cell growth and restoration of a normal phenotype.
13
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If in fact the lack of GJIC is causative of a lack of growth
control and cancer, then it would be predicted that a restoration
of GJIC in non-communicating, tumor cells should lead to growth
control and a normal phenotype. Transfection of several non-
communicating tumor cells has led to the restoration of GJIC and,
at least, partial growth control in vitro and in vivo (106-108) .
Stem Cells,—Gap Junctional Communication, Differentiation and
Carcinoqenesis
One of the critical assumptions to be made in cancer risk
assessment involves the problem, -Which cells are the target cells
for the carcinogenic process?' Is any cell in all tissues
potentially capable of being converted to a cancer cell? Or, is
4>
there only a few special types of cells capable of tumorigenic
transformation? One theory, namely, 'oncogeny as partially blocked
ontogeny (109) , supported by the theory of 'cancer as a disease of
differentiation' (110,111), leads one to hypothesize that stem
cells are the target cells. Since stem cells are defined as those
giving rise to one cell that goes down a differentiation pathway
and another cell retaining stem cell properties.
Evidence showing only certain cells are transformable came
from the observations by T'so and colleagues (112); They showed
only a few 'contact insensitive' cells were the ones which could be
transformed by carcinogens. Since cancer cells are characterized
by, not only their lack of growth control, but also by the
inability to terminally differentiate under normal conditions.
Cancer cells appear to be stem cells having the ability to
proliferate but unable to control their growth or to differentiate.
14
-------
The ultimate stem cell, the fertilized egg or zygote appears to
lack GJIC. Only after the early stages of development, do these
cells express various gap junction genes (113), thereby creating a
cellular mechanism for growth control, tissue compartmentalization
and differentiation. Using this idea, normal human kidney and
breast epithelial stem cells have been isolated from human tissue
(114,115). The question now arises that if normal stem cells do
not have GJIC and cancer cells do not have GJIC, then why aren't
normal stem cells cancerous? The answer appears to be that normal
stem cells can be induced to express GJIC very easily and they then
become progenitor and differentiated cells. Moreover, they
probably control their cell proliferative activity by extracellular
communication mechanism, that is, extra-cellular negative growth
regulators, such as TGF-fr from the differentiated daughter cells,
might suppress'stem cell growth (116-119). If either the negative
growth regulator is reduced or mutated or that the receptor on the
stem cell is reduced or mutated, the stem cell could grow in an
uncontrolled manner. If the stem cell cannot regulate its GJIC, it
probably cannot differentiate properly. These might be the
biological control points affected by the carcinogenic process.
A very significant observation has been recently made, in that
tumorigenic and non-communicating glioma cells could be growth-
arrested when co-cultured with sister glioma cells transfacted with
a connexin43 gene (120) . These transfected glioma cells, with the
expressed connexin43, were able to establish GJIC. However, the
growth arrest of the tumorigenic and non-communicating glioma cells
was via a soluble factor, produced by the newly communicating,
15
-------
transfected glioma cells, not by GJIC between the tumorigenic
glioma cells (which do not have GJIC) and the transfected glioma
cells. In other words, growth arrest was via an extra-cellular
communication mechanism (negative growth regulator) between
heterologous cells, produced- by inter-cellular communication
between homologous cells.
The implication of this study could explain (a) the selective
nature of metastatic cells, and (b) the growth control of non-
communicating stem cells by communicating differentiated daughter
cells. In the former example, if a non-communicating tumor cell
lands in a distal tissue which produces an effective negative
growth regulator, the tumor cell would not grow. On the other
hand, if the metastatic cell invades a tissue that lacks a negative
extracellular growth regulator, it would continue to pr-oliferate.
This could explain the "seed and soil- concept of tumor metastasis
(121).
In the latter case, a normal non-communicating stem cells,
surrounded by communicating differentiated daughters could produce
a negative extra-cellular growth regulator keeping the stem from
dividing. Removal or blockage of the source of the negative
extracellular growth regulator would allow the stem cells to
proliferate and differentiate.
If the preceding hypothesis is correct, then the number of
stem cells in a given tissue and during the developmental and aging
process. The question which needs to be answered is, -Are the
number of stem cells in all tissues the same during development and
aging?" For example, open-ended tissues, such as the skin and
16
-------
lining of the 'GI tract, have their stem cells constantly
proliferating until death. On the other hand, organs, such as the
lung and testes, must maintain their volume as well as replace lost
cells. Other organs, such as the liver and kidney, must primarily
maintain the volume of the organ once they finish their growth.
The human breast might provide the evidence linking the stem cell
as the target cell for carcinogenesis. Evidence for ' the risk to
cancer in the survivors to a bomb radiation seems to be highest in
young women who developed breast cancer (122). One explanation
might be that the number of stem cells of the breast tissue is
highest in these women, particularly if they have never conceived.
Pregnancy would be expected, in the breast tissue to deplete the
stem cell pool by virtue of converting these stem cells to milk
producing terminally differentiated cells. Again, if this
hypothesis is true, then there might be a need to consider the
number of stem cells available for transformation in each tissue
during each developmental state of the individual. This would
influence the initiation phase of carcinogenesis. One would predict
that as one ages, the number of stem cells in some tissues would
decrease, yet at the same time, as we age, the number of initiated
stem cells in these tissues would increase (spontaneously or by
induction). The critical issue to be considered then, would be the
amount of promotion that occurs after initiation.
SUMMARY; IMPLICATIONS TO RISK ASSESSMENT
Regulation of cell growth, development/differentiation,
homeostasis and adaptive responses to physical, chemical and
biological agents in a metazoan can occur at all levels of the
17
-------
biological hierarchy. At the cell level, extra-, intra- and
intercellular communication mechanisms help maintain homeostatic
regulation of these important functions [Figure 2]. These
ENVIRONMENTAL
CHEMICALS
[TPA. DOT.SACCHARIN,
PHENOSARBCTALI
(HORMONES, SHOWTH FACTORS.
NEUHOTRANSMITTER3. ETC ]
EXTRACELLULAR
COMMUNICATION
INTRACELLULAR
COMMUNICATION
SAP JUNCTION
INTERCELLULAR COMMUNICATION
© ALTERS MEMBRANE
FUNCTION
® ACTIVATES INACTIVE
PROTEINS
© MODULATES SAP
JUNCTION FUNCTION
© MODULATES GENE
EXPRESSION
Fig. 2 The heuristic schemata"'characterized the
postulated link between extracellular communication and
intercellular communication via various intracellular
transmembrane signalling mechanisms. It provides an
integrating view of how the neuroendocrine-immune system
("mind or brain/body connection") and other multi-system
coordinations could occur. While not shown here,
activation or altered expression of various oncogenes (an
"anti-oncogenes") could also contribute to the regulation
of gap junction function. (Reprinted from J.E. Trosko and
C.C. Chang, Toxicology Letters 49:283-295, 1989, Elsevier
Science Publisher; used with permission)
communication mechanisms have evolved via evolution to be
intimately integrated, such that perturbations of one communication
mechanism will affect the other communication processes (123). In
other words, hormonal type-extracellular communication can modulate
gap junctional intercellular communication via alterations in
18
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intracellular communication second messages, such as c-AMP
increases. The normal homeostatic control of these integrated.
communication processes can be disrupted by exogenous factors which
either mimic, in part, the endogenous extracellular communicating
signals or interfere with their ability to act. Stable interference
of this integrative communication process can also occur when the
genetic information coding for any of the three communication
networks is either mutated, eliminated by cell removal/cell death
or epigenetically altered.
The question of thresholds or lack of thresholds needed to
a.lter the homeostasis at any of these levels (molecular to
systems), which could bring about a disease state, needs to be
answered. Resolution, of this problem will not be easy. On one
hand, protective systems, redundant genetic information and DNA
repair systems seem to suggest that both mutagenesis and cell
killing, as cellular endpoints, would not demonstrate non-threshold
responses. As to whether epigenetic events could occur with non-
threshold kinetics, one can only speculate. If the homeostatic
state of a cell in a multicellular organism is one where the cell
is either in the G state with a given set of genes expressed or a
differentiated cell which is not responding to and adaptive
stimuli, one can assume that the intercellular communication
network has not been perturbed. Therefore, if intercellular
communication is -modulated, either up or down, one could expect the
cells to respond by turning on or off the genes necessary for cell
proliferation, cell differentiation or adaptive differentiation
responses. Evidence that gene expression is altered by alteration
19
-------
of. intercellular communication is almost self evident. There is
even evidence that threshold levels of growth factors, hormones,
oncogenes and chemical tumor promoters are needed to modulate GJIC
(92,124-128).
To complicate the matter of the relevance of GJIC to risk
assessment after exposure to toxic agents is,the role of stem cells
to various disease states that • are the result of not one
dysfunctional cell, but the consequence of a dysfunctional cell
having been amplified in _a given tissue so as to make its presence
known to the whole body by its ability to" disrupt homeostasis at
the systems level. The number of stem cells in various tissue
during aging is one crucial parameter and the accumulation of those
stem cells which have been blocked in their ability to terminally
differentiate, but not in the ability to proliferate, will be
potential determinants of future disease states.
The endpoint of GJIC should be seriously considered in the
assessment of toxic agent exposure. Abnormal GJIC has been
associated with a wide variety of disease states. Since GJIC has
been associated with development, differentiation and wound healing
(68,113,129-131), it should not be surprising to note that their
dysfunction has been associated with teratogenesis (132,133),
neurotoxicity (134), reproductive dysfunction (135), cardiovascular
diseases (136,137), cataract formation (138), ischemia,
hypertension (139,140), cholestasis (130), hereditary
mucoepithelial dysplasia (141), as well as Chagas disease (142).
These gap junctions exist in all tissues of the body. Eac.h type of
gap junction protein is probably regulated differently and in
20
-------
different cell types, the same gap junction might be regulated
differently due to the physiological states of the different cell
types. The interaction of multiple endogenous and/or exogenous
chemicals could, by the net result of that interaction,
synergistically, additively or antagonistically modulate GJIC. To
ignore this fundamental structure in the maintenance of homeostasis
of health of the human being will be to ignore one of the
parameters of a biologically-based risk assessment model.
ACKNOWLEDGEMENTS
The authors wish to acknowledge the excellent technical
assistance of Ms. Heather Rupp and Mrs. Beth Lockwood for some of
the research on which this paper was based and the skilled word
processing of Mrs. Jeanne McHugh. The research, on which this
manuscript is based, was supported, in part, by grant from the U.S.
Air Force Office of Scientific Research (USAFOSR-89-0325), the
NIEHS (1P42ES04911) and the National Cancer Institute (CA21104).
REFERENCES
1. Weinstein, I.E. Mitogenesis is only one factor in
carcinogenesis. Science, 251: 387-388, 1991.
2. Trosko, J.E. and Chang, C.C. Implications for risk assessment
of genotoxic and non-genotoxic mechanisms in carcinogenesis.
in: V.B. Vouk, G.C. Butler, D.G. Hoel and D.B. Peakall (eds.),
Methods for Estimating Risk of Chemical Injury: Human and
Non-Human Biota Ecosystems, pp. 181-200, Chichester, England:
John Wiley and Sons. 1985.
21
-------
3. Trosko. J.E. and Chang, C.C. The role of inhibited
intercellular communication in carcinogenesis: Implications
for risk assessment from exposure to chemicals. In: C.C.
Travis (ed.), Biologically Based Methods for Cancer Risk
Assessment, pp. 165-179, New York: Plenum Press. 1989.
4. Brody, H. A systems view of man: Implications for medicine,
science and ethics. Perspect Biol Med, 17: 71, 1973.
5. Potter, V.R. Probabilistic aspects of the human cybernetic
machine. Perspect Biol Med, 17: 164, 1974.
6. Trosko, J.E. and Chang, C.C. The role of DNA repair capacity
and somatic mutations in carcinogenesis and aging. In: H.T.
Blumenthal (ed.), Handbook of Diseases of Aging, pp. 252-295,
New York: Van Nostrand Reinhold Co. 1983.
7. Wyllie, A.H. Apoptosis and the regulation of cell numbers in
normal and neoplastic tissues. Cancer Metastasis Rev, 11:
95-103, 1992.
8. Williams, G.T. Programmed cell death: Apoptosis and
•oncogenesis. Cell, 65: 1097-1098, 1991.
9. Trosko, .J.E. and Chang, C.C. Role of intercellular
communication in modifying the consequences of mutations in
somatic cells. In: D.M. Shankel, P.D. Hartman, T. Kada and A.
Hollaender (eds.), Antimutagenesis and Anticarcinogenesis
Mechanisms, pp. 439-456, New York: Plenum Publ. 1986.
22
-------
10. Trosko, J.E., Chang, C.C., and Madhukar, B.V. In vitro
analysis of modulators of intercellular communication:
Implications for biologically based risk assessment models for
chemical exposure. Toxicol. In Vitro, 4: 635-643, 1990.
11. Trosko, J.E., Chang, C.C., Madhukar, B.V., and Oh, S.Y.
Modulators of gap junction function: The scientific basis of
epigenetic toxicology. In Vitro Toxicol., 3: 9-26, 1990.
12. Trosko, J.E. A failed paradigm: Carcinogenesis is more than
mutagenesis. Mutagenesis, 3: 363-366, 1988.
13. Trosko, J.E. A new paradigm is needed in toxicological
evaluation. Environ. Mutagen., 6: 767-769, 1984.
14. Clive, D. Genetic .toxicology: From theory to practice. J.
Clin. Res. Drug Devel., 1: 11-41, 1987.
15. Clayson, D.B. The need for biological risk assessment in
reaching decisions about carcinogens. Mutation Research, 185:
243-269, 1987. .
16. Douglas, G.R., Blakey, D.H., and Clayson, D.B. Genotoxicity
tests as predictors of carcinogens: An analysis. Mutation
Research, 196: 83-93, 1988.
23
-------
17. Ashby, j. Origins of current uncertainties in
carcinogen/mutagen screening. Environ Mol Mutagen, 16: 51-59,
1989.
18. Ames, B.N., Magaw, R., and Gold, L.S. Ranking possible
carcinogenic hazards. Science, 236: 271-280, 1987.
19. Travis, C.C., pichter Pack, S.A., Saulsbury, A.W., and
Yambert, N.W. Prediction of carcinogenic potency from
toxicological data. MUTAT. RES., 241: 21-36, 1990.
20. Goodman, J.I. Letter to Editor. Molec. Carcinogenesis, 5:
247-248, 1992.
21. Cohen, S.M. and Ellwein, L.B. Cell proliferation in
carcinogenesis. Science, 249: 1007-1011, 1990.
22. Ames, B.N. and Gold, L.S. Too many rodent carcinogens:
Mitogenesis increases mutagenesis. Science, 249: 970-971,
1990. ,
23. Trosko, J.E. Towards understanding carcinogenic hazards: A
crisis in paradigms. J. AM. COLL. TOXICOL., 8: 1121-1132,
1989.
24. Shields, P.G. and Harris, C.C. Molecular epidemiology and the
genetics of environmental cancer. J. A. M. A., 266: 681-687,
1991. • :
24
-------
25. Jones, P.A., Buckley, J.D., Henderson, B.E., Ross, R.K., and
Pike, M.C. From gene to carcinogen: A rapidly evolving field
in molecular epidemiology. CANCER RES., 51: 3617-3620, 1991.
26. Vogelstein, B. and Kinzler, K.W. Carcinogens leave
fingerprints. Nature, 355: 209-210, 1992.
27. Trosko, J.E. Does radiation cause cancer? In: RERF Update, pp.
3-5, 1992.
28. Fialkow, P.J. Clonal origin of human tumors. Am. Rev. Med.,
30: 135-176, 1979.
29. Armitage, P. and Doll, R. The age distribution of cancer and
a multistage theory of carcinogenesis. Br. J. Cancer, 8: 1-12,
1954.
30. Cairns, J. Mutation and selection and the natural history of
cancer. Nature, 225: 197-200, 1975.
31. Ames, B.N., Durston, W.E., Yamasaki, E., and Lee, F.D.
Carcinogens are mutagens: A simple test system combining liver
homogenates for activation and bacteria for detection. Proc
Natl Acad Sci USA, 70: 2281-2285, 1973.
32. Maher, V.M. and McCormick, J.J. Effect of DNA repair on the
cytotoxicity and mutagenicity of UV irradiation and of
chemical carcinogens in normal and xeroderma pigmentosum
cells. In: J.M. Yuhas, R.W. Tennant and J.D. Regan (eds.),
25
-------
Biology of Radiation Carcinogenesis, pp. 129-145, New York:
Raven Press. 1976.
33. Kraemer, K.H. Oculo-cutaneous and internal neoplasms in
xeroderma pigmentosum: Implications for theories of
carcinogenesis. In: B. Pullman, P.O.P, Ts'o and H. Gelboin
(eds.), Carcinogenesis: Fundamental Mechanisms and
Environmental Effects, pp. 503-507, New York: D. Reidel
Publishing Co. 1980.
34. Glover, T.W., Chang, C.C., Trosko, J.E., and Li, S.S.L.
Ultraviolet light induction of diphtheria toxin
resistant-mutants in normal and xeroderma pigmentosum human
fibroblasts. Proc Natl Acad Sci USA, 76: 3982-3986, 1979.
35. Cleaver, J.E. and Trosko, J.E. Absence of excision of
ultraviolet-induced cyclobutane dimers in xeroderma
pigmentosum. Photochem. Photobiol., 11: 547-550, 1970.
36. Lieberman, M.W., Baney, R.N., Lee, R.E., Sell, S., and Farber,
E. Studies on DNA. repair in human lymphocytes treated with
ultimate carcinogens and alkylating agents. Cancer Research,
3: 1292-1306, 1971.
37. Weinberg, R.A. The action of oncogenes in the cytoplasm and
nucleus. Science, 230: 770-776, 1985.
26
-------
38. Bishop, J.M. Cellular oncogenes and retroviruses. Ann. Rev.
Biochem., 52: 301-354, 1983.
39. Weinberg, R.A. Tumor suppressor genes. Science, 254:
1138-1146, 1991.
40. Knudson, A.G. Hereditary cancer, oncogenes, and antioncogenes.
Cancer Research, 45: 1437-1443, 1985.
41. Pitot, H.C., Goldsworthy, T., and Moran, S. The natural
history of carcinogenesis: Implications of experimental
carcinogenesis in the genesis of human cancer. J. Supramol.
Struct. Cellul. Biochem., 17: 133-146, 1981.
42. Tennant, R.W., Margolin, B.H., Shelby, M.D., Zeiger, E.,
Haseman, J.K., Spalding, J., Caspary, W., Resnick, M.<
Stasiewicz, S., Anderson, B., and Minor, R. Prediction of
chemical carcinogenicity in rodents from in vitro genetic
toxicity assays. Science, 235: 933-941, 1987.
43. Nowell, P.C. The clonal evolution of tumor cell population.
Science, 194: 23-28, 1976.
44. Fialkow, P.J. Clonal origin and stem cell evolution of human
tumors. In: J.J. Mulvihill, R.W. Miller and J.F. Fraumerii
(eds.). Genetics of Human Cancer, pp. 439-453, New York: Raven
"press. 1977.
27
-------
45. Trosko, J.E., Chang, c.C., and Madhukar, B.V. Cell-cell
communication: Relationship of stem cells to the carcinogenic
process. In: D.E. Stevenson, J.A. Popp, J.M. Ward, R.M.
McClain, T.J. siaga and H.C. Pitot (eds.), Mouse Liver
Carcinogenesis: Mechanisms and Species Comparisons, pp.
259-276, New York: Alan R. Liss, Inc.. 1990.
46. Klein-Szanto, A.J.P. and Slaga, T.J. Effects of peroxides on
rodent skin: Epidermal hyperplasia and tumor promotion. J.
Invest. Dermatol.,. 79: 30-34, 1982.
47. Argyris, T.S. Tumor promotion by regenerative epidermal
hyperplasia in mouse skin. J. Cutaneous Pathol., 9: 1-18,
1982.
48. Frei, J.v. and Stephens, P. The correlation of promotion of
tumour growth and of induction of hyperplasia in epidermal
two-stage Carcinogenesis. Br. J. Cancer, 22: 83-92, 1968.
49. Trosko, J.E., Chang, C.C., and Medcalf, A. Mechanisms of tumor
promotion: Potential role of intercellular communication.
Cancer Invest, 1: 511-526, 1983.
50. Trosko, J.E. and Chang, C.C. Nongenotoxic mechanisms in
Carcinogenesis: Role of inhibited intercellular communication.
In: R.w. Hart and F.G. Hoerger (eds.), Banbury Report 31:
Carcinogen Risk Assessment: New Directions in the Qualitative
28
-------
and Quantitative Aspects, pp. 139-170, Cold Spring Harbor, NY:
Cold Spring Harbor Laboratory Press. 1988.
51. Trosko, J.E. and Chang, C.C. Gap junctional intercellular
communication in neoplasia: Implications for the cause and
treatment of cancer. In: L. Herrera (ed.), Familial
Adenomatous Polyposis, pp. 2.79-288, New York: Alan R. Liss,
Inc.. 1990.
52. Trosko, J.E. and Chang, C.C. Stem cell theory of
carcinogenesis. Toxicol. Lett., 49: 283-295, 1989.
53. Potter, V.R. Use of two sequential applications of initiators
in the production of hepatomas in the rat: An examination of
the Solt-Farber Protocol. Cancer Research, 44: 2733-2736,
1984.
54. Hennings, H., Shores, R., Wenk, M.L., Spangler, E.F., Tarone,
R., and Yuspa, S.H. Malignant conversion of mouse skin tumors
is increased by tumor initiators and unaffected by tumor
promoters. Nature, 304: 67-69, 1983.
55. Reddy, A.L. and Fialkow, P.J. Papillomas induced by
initiation-promotion differ from those induced by carcinogen
alone. Nature, 304: 69-71, 1983.
29
-------
56. O'Connell, J.F., Klein-Szanto, A.J.P., Digiovanni, D.M. ,
Fries, J.M., and Slaga, T.J. Malignant progression o£ mouse
skin papillomas treated with ethylnitrosourea, NMNG, or TPA.
Cancer Lett, 30: 269-274, 1986.
57. Taguchi, T., Yokoyama, M., and Kitamura, Y. Intraclonal
conversion from papilloma to carcinonma in the skin of
PgK-l'/PgK-l6 mice treated by a complete carcinogenesis process
or by an initiation-promotion regimen. Cancer Research, 44:
3779-3782, 1984.
58. Trosko, J.E., Madhukar, B.V., and Chang, C.C. Endogenous and
exogenous modulation of gap junctional intercellular
communication: Toxicological and pharmacological implications.
Life Sciences, 1993 (in press).
59. Levine, E.M., Becker, Y. , Boone, C.W., and Eagle, H. Contact
inhibition, macromolecular synthesis, and polyribosomes in
cultured human diploid fibroblasts. Proc Natl Acad Sci USA,
53: 350-355, 1965.
«
60. Abercrombie, M. Contact inhibition and malignancy. Nature,
281: 259-262, 1979.
61. Borek, C. and Sachs, L. The difference in contact inhibition
of cell replication between normal cells and cells transformed
30
-------
by different carcinogens. Proc Natl Acad Sci USA, 55:
1705-1711, 1966.
62. Kanno, Y. Modulation of cell communication and carcinogenesis.
Jpn J Physiol, 35: 693-707, 1985.
63. Loewenstein, W.R. Permeability of membrane junctions. Ann N Y
Acad Sci, 137: 441-472, 1966.
64. Fentman, I.S., Hurst, J-', Ceriani, R.L., and
Taylor-Papadimitriou, J. Junctional intercellular
communication pattern of cultured human breast cancer cells.
Cancer Research, 39: 4739-4743, 1979.
65. Yamasaki, H., Hollstein, M., Mesnil, M., Martel, N., and
Aguelon, A.M. Selective lack of intercellular communication
between transformed and nontransformed cells as a common
property of chemical and oncogene transformation of BALB/c3T3
cells. Cancer Research, 47: 5658-5664, 1987.
66. Saez, J.C., Spray, D.C.% and Hertzberg, E.L. Gap junctions:.
Biochemical properties and functional regulation under
physiological and toxicological conditions. In Vitro Toxicol.,
3: 69-86, 1990.
31
-------
61. Pound, A.W. and McGuire, L.J. Repeated partial hepatectomy as
a promoting stimulus for carcinogenic response of liver to
. nitrosamines in rats. Br. J. Cancer, 37: 585-594, 1978.
68. Yancey, S.B., Easter, D., and Revel, J.P. Cytological changes
in gap junctions during liver regeneration. J. Ultrastruct.
Res., 67-. 229-242, 1979.
69. Massey, K.D., Minnich, B.N., and Burt, J.M. Arachidonic acid
and lipoxygenase metabolites uncouple neonatal rat cardiac
myocyte pairs. Am J Physiol, 263: C494-C501, 1992.
70. Fluri, G.S., Rudisuli, A., Willi, M., Rohr, S., andWeingart,
R. Effects of Arachidonic Acid on the Gap Junctions of
Neonatal Rat Heart Cells. Pflugers Arch, 417: 149-156, 1990.
71. Trosko, J.E., Madhukar, B.V., Hasler, C., and Chang, C.C.
Modulated intercellular communication: Consequences of
extracellular molecules triggering intracellular
communication. In: K.V. Honn, L.J. Marnett, S. Nigam and T.
Walden (eds.), Eicosanoids and Other Bioactive Lipids in
Cancer and Radiation Injury, pp. 285-295, Boston: Kluwer Acad.
Publ. 1989.
72. Agarwal, R. and Daniel, E.E. Control of gap junction formation
in canine trachea by arachidonic acid metabolites. Am J
Physiol, 250: 495-505, 1986.
32
-------
73. Trosko, J.E., Jone, C., and Chang, C.C. The role of tumor
promoters on phenotypic alterations affecting intercellular
communication and tumorigenesis. Ann N Y Acad Sci, 407:
316-327, 1983.
74. Chen, T.H., Kavanagh, T.J., Chang, C.C., and Trosko, J.E.
Inhibition of metabolic cooperation in Chinese hamster V79
cells by various organic solvents and simple compounds. Cell
Biol Toxicol, 1: 155-171, 1984.
75. Columbano, A., Ledda-Columbano, G.M., Ennas, M.G., Curto, M.,
Chelo, A., and Pani, P. Cell proliferation and promotion of
rat liver carcinogenesis: Different effect of hepatic
regeneration and mitogen induced hyperplasia on the
development of enzyme-altered foci. Carcinogenesis, 11:
771-776, 1990.
76. Parkinson, E.K. and Emmerson, A. Non-promoting
hyperplasiogenic agents do not mimic the effects of phorbol,
12-myristate, 13-acetate on terminal differentiation of normal
and transformed human keratinocytes. Carcinogenesis, 5:
687-690, 1984.
77. Sisskin, E.E., Gray, T., and Barrett, J.C. Correlation between
sensitivity to tumor promotion and sustained epidermal
hyperplasia of mice and rats. Carcinogenesis, 3: 403-407,
1982.
33
-------
78. Rodrigueztarduchy, G. and Lopezrivas, A. Phorbol Esters
Inhibit Apoptosis in IL-2-Dependent Lymphocytes-T. BIOC. BIOP.
R., 164: 1069-1075, 1989.
79. Moolgavkar, S.H. Multistage models for cancer risk assessment.
In: C.C. Travis fed.), Biologically Based Methods for Cancer
Risk Assessment, pp. 9-20, New York: Plenum Press. 1989.
80. Atkinson, M.M., Menko, A.S., Johnson, R.G., Sheppard, I.R.,
and Sheridan, J.D. Rapid and reversible reduction of
junctional permeability in cells infected with a temperature
sensitive mutant of avian sarcoma virus. J Cell Biol, 9:
573-578, 1981.
81. Atkinson, M.M., Anderson, S.K., and Sheridan, J.D.
Modification of gap junctions in cells transformed by a
temperature-sensitive mutant of Rous sarcoma virus. J Membr
Biol, 91: 53-64, 1986.
82. Azarnia, R. and Loewenstein, W.R. Intercellular communication
and the control of growth: Alteration of junctional
permeability by the src gene - A study with
temperature-sensitive mutant Rous sarcoma virus. J Membr Biol,
82: 191-205, 1984.
83. Azarnia, R., Reddy, S., Kimiecki, T.E., Shalloway, D., and
Loewenstein, W.R. The cellular src gene product regulates
34
-------
junctional cell-to-cell communication. Science, 239: 398-401,
1988.
84. Azarnia, R. and Loewenstein, W.R. Polyomavirus middle T
antigen deregulates junctional cell to cell communication.
Mol Cell Biol, 7: 946-950, 1987.
85. Chang, C.C., Trosko, J.E.. Kung, H.J., Bombick, D., and
Matsumura, F. Potential role of the src gene product in
inhibition of gap junctional communication in NIH 3T3 cells.
Proc Natl Acad Sci USA, 82: 5360-5364, 1985.
86. Dotto, G.P., El-Fouly, M.H., Nelson, C., and Trosko, J.E.
Similar and synergistic inhibition of gap-junctional
communication by ras transformation and tumor promoter
treatment of mouse primary keratinocytes. Oncogene, 4:
637-641, 1989.
87. Bignami, M., Rosa, S., Falcone, G., Tato, F., Katoh, F., and
Yamasaki, H. Specific viral oncogenes cause differential
effects on cell-to-cell communication relevant to the
suppression of the transformed phenotype by normal cells.
Molec. Carcinogenesis, 1: 67-75, 1988.
88. El-Fouly, M.H., Trosko, J.E., Chang, C.C., and Warren, S.T.
Potential role of the human Ha-ras oncogene in the inhibition
35
-------
of gap junctional intercellular communication. Mol Carcinog,
2: 131-135, 1989.
89. El-Fouly, M.H., Trosko, J.E., and Chang, C.C. Phenotypic
transformation and inhibition of gap-junctional intercellular
communication in epithelial and mesenchymal cells by the neu
oncogene. 4th Annual Oncogene Meeting, Frederick, MD: July
5-9, 1988.(Abstract)
90. Kalimi, G.H., Hampton, L.L., Trosko, J.E., Thorgeirsson, S.S.,
and Huggett, A.C. Homologous and heterologous gap-junctional
intercellular communication in v-raf-, v-myc-, and
v-raf/v-myc-transducted rat liver epithelial cell lines. Mol
Carcinog, 5: 301-310, 1992.
91. de Feijter, A.W. ,• 'Ray, J.S., Weghorst, C.M., Klaunig, J.E.,
Goodman, J.I., Chang, C.C., Ruch, R.J., and Trosko, J.E.
Infection of rat liver epithelial cells with v-Ha-ras:
Correlation between oncogene expression; gap junctional
communication, and tumorigenicity. Mol Carcinog, 3: 54-67,
1990.
92. de Feijter, A.W., Trosko, J.E., Krizman, D.B., Lebovitz, R.M.,
and Lieberman, M.W. Correlation of increased levels of Ha-ras
T24 protein with extent of loss of gap junction function in
rat liver .epithelial cells. Mol Carcinog, 5: 205-212, 1992.
36
-------
93. Kalimi, G., Chang, C.C., Edwards, P., Dupont, E., Madhukar,
B.V., Stanbridge, E., and Trosko, J.E. Re-establishment of gap
junctional communication in a non-tumorigenic Hela-normal
human fibroblast hybrid. Proc. Am. Assoc. Cancer Res., 31:
319, 1990.
94. Lee, S.W., Tomasetto, C., and Sager, R. Positive selection of
candidate tumor-suppressor genes by subtractive hybridization.
Proc. Natl. Acad. Sci. USA, 88: 2825-2829, 1991.
95. Mehta, P.P., Bertram, J.S., and Loewenstein, W.R. The actions
of retinoids on cellular growth correlate with their actions
on gap junctional communication. J. CELL BIOL., 108:
1053-1065, 1989.
96. Mehta,, P.P., Bertram, J.S., and Loewenstein, W.R. Growth
inhibition of transformed cells correlates with the junctional
communication with normal cells. Cell, 44: 187-196, 1986.
97. Rogers, M., Berestecky, J.M., Hossain, M.Z., Quo, H.M., Kadle,
R., Nicholson, B.J.,and Bertram, J.S. Retinoid-Enhanced Gap
Junctional Communication Is Achieved by Increased Levels of
Connexin-43 Messenger RNA and Protein. Mol. Carcinogen., 3:
335-343, 1990.
98. Brummer, F., Zempel, G., Buhle, P., Stein, J.C., and Hulser,
D.F. Retinoic acid modulates gap junctional permeability: A
37
-------
comparative study of dye spreading and ionic coupling in
cultured cells. Exp. Cell Res., 196: 158-163, 1991.
99. Rivedal, E. and Sanner, T. Regulation of gap junctional
communication in Syrian hamster embryo cells by retinoic acid
and 12-O-tetradecanoyl-phorbol-13-acetate. Carcinogenesis, 13:
199-203, 1992.
»
100. Azarnia, R. and Russell, T.R. Cyclic AMP effects on cell to
cell junctional membrane permeability during adipocyte
differentiation of 3T3-L1 fibroblasts. J. CELL BIOL., 100:
265-269, 1985.
101. Flagg-Newton, J.L., Dahl, G., and Loewenstein, W.R. Cell
junction and cyclic AMP: Upregulation of junctional membrane
permeability and' junctional membrane particles by
administration of cyclic nucleotide or phosphodiesterase
inhibitor. J. Mernb. Biol., 63: 105-121, 1981.
102. Demaziere, A.M.G.L. and Scheuerman, D.W. Increased gap
junctional area in the rat liver after administration of
dibutyrl cAMP. Cell Tiss Res, 239: 651-655, 1985.
103. Veld, P.I., Schuit, F., and Pipeleers, D. Gap junctions
between pancreatic B-cells are modulated by cyclic AMP. Eur.
J. Cell Biol., 36: 269-276, 1985.
38
-------
104. Zhang, L.X., Cooney, R.V., and Bertram, J.S. Carotenoids
enhance gap junctional communication and inhibit lipid
peroxidation in C3H/10T1/2 cells: Relationship to their cancer
chemopreventive action. Carcinogenesis, 12: 2109-2114, 1991.
105. Ruch, R.J., Madhukar, B.V., Trosko, J.E., and Klaunig, J.E.
Reversal of ras-induced inhibition of gap junctional
intercellular communication, transformation and tumorigenesis
by lovastatin. Molec. Carcinogenesis, 1992.(in press)
106. Fishman, G.I., Moreno, A.P., Spray, D.C., and Leinwand, L.A.
Functional analysis of human cardiac gap junction channel
mutants. Proc. Natl, Acad. Sci. USA, 88: 3525-3529, 1991.
107. Zhu, D., Caveney, S., Kidder, G.M., and Naus, C.C.G.
Transfection of C6 ' glioma cells with Connexin-43 cDNA:
Analysis of expression, intercellular coupling, and cell
proliferation. Proc. Natl. Acad. Sci. USA, 88: 1883-1887,
1991.
•i
108. Eghbali, B., Kessler, J.A., and Spray, D.C. Expression of gap
' junction channels in communication-incompetent cells after
stable transfection with cDNA encoding connexin-32. P. NAS.
US., 87: 1328-1331, 1990.
39
-------
109. Potter, V.R. Phenotypic diversity in experimental hepatomas:
The concept of partially blocked ontogeny. Br. J. Cancer, 35:
1-23, 1978.
110. Pierce, G.B. Neoplasms, differentiation and mutations. Am. J.
Pathol., 77: 103-118, 1974.
111. Markert, C. Neoplasia: A disease of cell differentiation.
Cancer Research, 28i 1908-1914, 1968.'
112. Nakano, S., Ueo, H., Bruce, S.A., and Ts'o, P.O.P. A
contact-insensitive subpopulation in Syrian hamster cell
cultures with a -greater susceptibility to chemically induced
neoplastic transformation. Proc Natl Acad Sci USA, 82:
5005-5009, 1985.
113. Lo, C.W. Communication compartmentation and pattern formation
in development. In: M.V.L. Bennett and D.C. Spray (eds.)-, Gap
Junctions, pp. 251-264, Cold Spring Harbor, New York: Cold'
Spring Harbor Laboratory. 1985.
114. Chang, C.C., Trosko, J.E., El-Fouly, M.H., Gibson-D'Ambrosio,
R.E., and D'Ambrosio, S.M. Contact insensitivity of a
subpopulation of normal human fetal kidney epithelial cells
and of human carcinoma cell lines. Cancer Research, 47:
1634-1645, 1987.
40
-------
115. Chang, C.C., Nakatsuka, S., Kalimi, G., Trosko, J.E., and
Welsch, C.W. Characterization of two types of normal human
breast epithelial cells that are either deficient or
proficient in gap junctional intercellular communication. J
Cell Biochem Suppl, 14B: 331, 1990.
116. Keski, J. and Moses, H.C. Growth inhibitory polypeptide in the
regulation of cell proliferation. Med. Biol., 65: 13-20, 1987.
117. Sonnenschein, C., Olea, N., Pasanen, M.E., and Soto, A.M.
Negative controls of cell proliferation: Human prostate cancer
cells and androgens. Cancer Research, 49: 3474-3481, 1989.
118. Kimchi, A., Wang, X.-F., Weinberg, R.A., Cheifetz, S., and
Massague, J. Absence of TGF-beta receptors and growth
inhibitory responses' in retinoblastoma cells. Science, 240:
196-198, 1988.
119. de Rooij, D.G., Lok, D., and Weenk, D. Feedback regulation of
the proliferation of the undifferentiated spermatogonia in the
Chinese hamster by the differentiating spermatogonia. Cell-
Tissue Kinet, 18: 71-81, 1985.
120. Zhu, D.G., Kidder, G.M., Caveney, S., and Naus, C.C.G. Growth
Retardation in Glioma Cells Cocultured with Cells
Overexpressing a Gap Junction Protein. Proc Natl Acad Sci USA,
89: 10218-10221, 1992.
41
-------
121. Paget, s. The distribution of secondary growth in cancer of
the breast. Lancet, '!• 571-573, 1889.
122. Tokunaga, M. , Land, C.E., and Tokuoka, S. Follow-up studies
of breast cancer incidence among atomic bomb survivors. j
Radiat Res (Suppl.) 32:201-211, 1991.
123. Trosko, J.E., Chang, C.C., Madhukar, B.V., and Klaunig, J.E.
Chemical, oncogene and growth factor inhibition of gap
junction intercellular communication: An integrative
hypothesis of carcinogenesis. Pathobiol., 58: 265-278, 1990.
124. Verma, A.K. and Boutwell, R.K. Effects of dose and duration of
treatment with the tumor promotiong agent, TPA, on mouse skin
carcinogenesis. Carcinogenesis, 1: 271-276, 1986.
125. Goldsworthy, T., Campbell, H., and Pi tot, H.C. The natural
history and dose-response characteristics of enzyme-altered
foci in rat liver following phenobarbital and
diethylnitrosamine administration. Carcinogenesis, 5: 67-71,
1984.
126. Demi, E. and Oesterle, D. Dose response of promotion by
polychlorinated biphenyls and chloroform in rat liver foci
bioassay. Arch Toxicol, 60-. 209-211, 1987.
42
-------
127. Maekawa, A., Onodera, H., Ogasawara, H., Matsushima, Y.,
Mitsumori, K.,' and Hayashi, Y. Threshold dose dependence in
phenobarbital promotion of rat hepatocarcinogenesis initiated
by diethylnitrosamine. Carcinogenesis, 13: 501-503, 1992.
128. Pereira, M.A., Herren-Freund, S.L., and Long, R.E.
Dose-response relationship of phenobarbital promotion of
diethylnitrosamine initiated tumors in rat liver.' CANCER
LETT., 32: 305-311, 1986.
129. Yancey, S.B., Biswal, S., and Revel, J.P. Spatial and temporal
patterns of distribution of the gap junction protein
connexin43 during mouse gastrulation and organogenesis.
«
Development, 114: 203-212, 1992.
130. Traub, O., Druge, P.M., and Willecke, K. Degradation and
resynthesis of gap junction protein in plasma membranes of
regenerating liver after partial hepatectoray'or cholestasis.
Proc Natl Acad Sci USA, 80: 755-759, 1983.
131. Bryant, P.J. and Fraser, S.E. Wound healing, cell
communication, and DNA synthesis during imaginal disc
regeneration in Drosophila. Dev Biol, 127: 197-208, 1988.
132. Trosko, J.E., Chang, C.C., and Netzloff, M. The role of
inhibited cell-cell communication in teratogenesis.
Teratogenesis Carcinog Mutagen, 2: 31-45, 1982.
43
-------
133. Warner, A.E., Guthrie, S.C., and Gilula, N.B. Antibodies to
gap functional protein selectively disrupt junctional
communication in the early amphibian embryo. Nature, 311:
127-131, 1984.
134. Trosko, J.E., Jone, C., and Chang, C.C. Inhibition of gap
junctional-mediated intercellular communication in vitro by
Aldrin, Dieldrin, and Toxaphene: A possible cellular mechanism
for their turnor-promotiong and neurotoxic effects. Mol
Toxicol, 1: 83-93, 1987.
135. Ye, Y.X., Bombick, D., Hirst, K., Zhang, G.X., Chang, C.C,,
Trosko, J.E., and Akera, T. The modulation of gap junctional
communication by gossypol in various mammalian cell lines in
vitro. Fundam. Appl. Toxicol., 14: 817-832, 1990.
136. Saffitz, J.E., Hoyt, R.H., Luke, R.A., Kanter, H.L., and
Beyer, E.G. Cardiac myocyte interconnections at gap junctions
- Role in normal and abnormal electrical conduction. Trend.
Cardiovasc Med, 2: 56-60, 1992.
137. Kleber, A.G., Riegger, C.B., and Janse, M.J. Electrical
uncoupling and increase of extracellular resistance after
induction of ischemia in isolated, arterially perfused rabbit
papillary muscle. Circ Res, 61: 271-279, 1987.
44
-------
138. Tanaka, T., Sakai, M., Fujimoto, K., and Ogawa, K.
' Morphometric analysis of gap junctions in the rat lens during
cataract formation. Acta Histochem Cytochem, 23: 781-792,
1990.
139. Smith, J.H., Green, C.R., Peters, N.S., Rothery, S., and
Severs, N.J. Altered patterns of gap junction distribution in
ischemic heart disease - An immunohistochemical study of human
myocardium using laser scanning confocal microscopy. Am. J. .
Pathol., 139: 801-821, 1991.
140. Schellens, J.P., Blange, T., and de Groot, K. Gap junction
ultrastructure in rat liver parenchymal cells after invivo
ischemia. Virchows. Arch. [B], 53: 347-352, 1987.
141. Witkop, C.J., White, J.G., King, R.A., Dahl, M.V., Young,
W.G., and Sauk, J.J. Hereditary mucoepithelial dysplasia: A
disease apparently of desmospme and gap junction formation.
Am. J Hum. Genet., 31: 414-427, 1979.
142. Campos de Carvalho, A.C., Tanowitz, H.B., Wittner, M.,
Dermietzel, R., Roy, C., Hertzberg, E.L., and Spray, D.C. Gap
junction distribution is altered between cardiac myocytes
infected with Trypanosoma cruzi . Circ Res, 70: 733-742, 1992.
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BIOGRAPHICAL SKETCH: WILLIAM H. van der SCHALIE, Ph.D.
Dr. van der Schalie received his undergraduate degree in Biology from the Michigan
State University in 1973. His Ph.D. in Zoology was received from the Virginia
Polytechnic Institute and State University in 1977.
Dr. van der Schalie is currently a Biologist with the Risk Assessment Forum of the
United States Environmental Protection Agency in Washington, DC. As science
coordinator for the Risk Assessment Forum, he facilitates the activities of senior EPA
scientists. These activities are ultimately directed toward developing EPA's first
Agency-wide guidelines for ecological risk assessment. He has been in this position
since August of 1990. Prior to joining EPA, Dr. van der Schalie was a Biological
Sciences Administrator and a Branch Chief with the Research Methods Branch, Health
Effects Research Division, of the U. S. Army Biomedical Research and Development
Laboratory.
Dr. van der Schalie is an Associate Professor of Biology (part-time) at Hood College
in Frederick, MD. His past experience includes positions as a Research Aquatic
Biologist and a National Research Council Resident Research Associate at the U. S.
Army Biomedical Research and Development Laboratory. He was also a Teaching
Fellow at the University of Michigan Biological Station in Pellston, Ml.
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Annual Review
A FRAMEWORK FOR ECOLOGICAL
RISK ASSESSMENT AT THE EPA
SUSAN B. NoRTON.t DONALD J. RQDIER,! JOHN H. GENTILE,}
WILLIAM H. VAN DER SCHALIE,! WILLIAM P. WOOD!
and MICHAEL W. SLIMAK*!
fU.S. Environmental Protection Agency, Washington, DC 20460
JU.S. Environmental Protection Agency Laboratory Narragansett, Rhode Island 02592
(Received 9 January 1992; Accepted 13 July 1992)
Abstract-Ecological risk assessments evaluate the likelihood of adverse ecological effects caused
bv s Ss ors relatld to human activities such as draining of wetlands or release of chemicals The
?erm /rrSor i used to describe any chemical, physical, or biological entity that can induce adverse
eff«ts on ecological components (i.e., individuals, populations, commumt.es, or ecosystems). In
* s relSvart de a historical perspective on ecological riskassessment activities at theUS-Env,-
onmental Protection Agency (EPA) is followed by a discussion of the EPA s "*™^ **S
which describes the basic elements for conducting an ecological risk assessment. The Framework
Renort" is neither a procedural guide nor a regulatory requirement within the EPA. Rather, it is
Eded to foster'Iconsistent approach to ecological risk assessments withm the Agency, identify
key issues, and define terminology.
Keywords-Risk assessment Ecological stressors
INTRODUCTION
Environmental problems are often complex,
with multiple causes and diverse ecological effects.
Examples include the effects of global climate
change, habitat loss, acid deposition, and multiple
chemicals present in the environment. Dealing with
such problems requires a flexible decision-making
process that can accommodate this diversity while
providing some measure of the uncertainty associ-
ated with decisions that are made. At the U.S. En-
vironmental Protection Agency (EPA), there is
increasing interest in using ecological risk assess-
ments as a basis for environmental decisions.
This article examines the EPA's past, present,
and possible future utilization of ecological risk
assessment approaches. The core of the discussion
is a summary of the EPA's recently published
"Framework Report," which describes the basic
elements of, or a framework for, ecological risk
assessment and has been proposed as a basis for
conducting ecological risk assessment within the
EPA [1]. .
The "Framework Report" does not contain sub-
stantive guidance on factors that are integral to the
risk assessment process, such as analytical meth-
ods, techniques for analyzing and interpreting data,
•To whom correspondence may be addressed. -
or guidance on factors influencing policy. Such is-
sues are reserved for future guidance, and plans for
developing such guidance, based on the approach
described in the "Framework Report," are de-
scribed in the last section of this article. We hope
that this article will broaden the audience for the
"Framework Report" and help stimulate discus-
sions of the many issues that have been highlighted
throughout the guideline development process.
The nature of ecological risk assessment
Ecological risk assessments evaluate the likeli-
hood that adverse ecological effects will occur as
a result of exposure to stressors related to human.
activities, such as draining of wetlands or release of
chemicals. The term stressor is used here to describe
any chemical, physical, or biological entity that can
induce adverse effects on ecological components,
that is, individuals, populations, communities, or
ecosystems. Adverse ecological effects encompass
a wide range of disturbances ranging from mortal-
ity in an individual organism to a loss in ecosystem
function. Thus, the ecological risk assessment pro-
cess must be flexible while providing a logical and
scientific structure to accommodate a broad array
of stressors and ecological components.
Ecological risk may be expressed in a variety of
ways. Whereas some ecological risk assessments
1663
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1664
S.B. NORTON ET AL.
may provide true probabilistic estimates of risk,
others may be deterministic or even qualitative in
nature. In these cases, the likelihood of adverse ef-
fects is expressed through a. semiquantitative or
qualitative comparison of effects and exposure.
The appropriate application of risk assessment
helps meet the EPA's goal of targeting environmen-
tal protection resources at the problems and the
geographic areas posing the greatest risks. The fol-
lowing section briefly discusses the use and devel-
opment of ecological risk assessment approaches in
EPA programs.
Ecological risk assessment at EPA
Since its establishment in 1970, the EPA has at-
tempted to protect both human health and ecolog-
ical resources. Typically, however, activities most
closely related to human health have received the
highest priority. Nonetheless, assessment of risk to
ecological resources has been an important activ-
ity for many programs at the EPA. The recent re-
port by the Science Advisory Board [2] strongly
supported an increased emphasis on risk-based de-
cision making and ecological risk assessment. The
board's recommendations included that (a) the
EPA should target its environmental protection ef-
forts on the basis of opportunities for the greatest
risk reduction; (b) the EPA should attach as much
importance to reducing ecological risk as it does to
reducing human health risk; and (c) the EPA
should improve the data and analytical methodol-
ogies that support the assessment, comparison, and
reduction' of different environmental risks.
The ecological risk activities of two EPA of-
fices—the Office of Pesticides and Toxic Sub-
stances and th'e Office of Water—illustrate the use
and development of ecological risk assessment at
the EPA. The Office of Prevention, Pesticides and
Toxic Substances is concerned about potential im-
pacts of pesticides and toxic chemicals on organ-
isms, including aquatic and terrestrial communities.
Its legal mandates come from the Federal Insecti-
cide, Fungicide, and Rodenticide Act (FIFRA) and
the Toxic Substances Control Act (TSCA).
Both programs under the Office of Prevention,
Pesticides and Toxic Substances—the Office of
Pesticide Programs (OPP) and the Office of Pol-
lution Prevention and Toxics (OPPT)—assess risks
to ecological resources by an ecotoxicological ap-
proach: Laboratory toxicity bioassays are used to
characterize ecological effects; exposure is estimated
by using either monitoring data or models; and the
risk is estimated by comparing the two with a sim-
ple quotient, risk = exposure/effects. Although
the use of laboratory bioassays and the quotient
method have provided results useful for decision-
making with a reasonable commitment of resources,
there has been increased interest in strengthening
the assessment of ecological effects. Advances in-
clude incorporating toxicity. data for higher levels
of organization (e.g., through mesocosms), using
simulation models to project effects at lower tro-
phic levels to higher trophic levels, and developing
techniques to assess exposure to multiple chemicals.
The Office of Water is required by the Clean
Water Act to restore and maintain the biological
integrity of the nation's waters and, specifically, to
ensure the protection and propagation of a balanced
population of fish, shellfish, and wildlife. The EPA
also develops methods, including biological mon-
itoring and assessment methods, for establishing
and measuring water-quality criteria. These statu-
tory requirements have encouraged the Office of
Water to develop innovative approaches to eco-
logical assessment. The Water Quality Act of 1987
(Public Law 100-4) amends the decade-old Clean
Water Act and redirects its focus from the technol-
ogy approach, based on end-of-pipe standards, to
full-scale implementation of the water-quality ap-
proach, based on ambient receiving water standards.
State water-quality standards and designated
uses form the backbone of the water quality-based
approach, and the EPA criteria are developed as
national recommendations to assist states in devel-
oping their standards. The most commonly used
risk-based approaches to the evaluation of water
quality are the application of chemical-specific wa-
ter-quality criteria and whole-effluent toxicity cri-
teria. Both criteria have three components, the first
characterizing the ecological effects and the latter
two assessing the exposure:
1. Magnitude—what concentration of a pollutant
(or a pollutant parameter such as toxicity) is
allowable
2. Duration-the period of time over which the
predicted in-stream concentration occurs (this
specification limits the duration of concentra-
tion above the criteria)
3. Frequency - how often criteria can be exceeded
without unacceptably affecting the community.
The approaches to risk-based water-quality crite-
ria are being expanded by the Office of Water in
their development of several new types of criteria,
including chemical-specific sediment criteria and
wildlife criteria, and biological criteria based on
community structure.
-------
Framework for ecological risk, assessment at EPA
1665
Although the programs in the Office of Preven-
tion, Pesticides and Toxic Substances and the Of-
fice of Water have a longer history, all of the EPA's
programs are beginning to address ecological con-
cerns. Responding to the increased emphasis on
ecological issues and the need to provide for some
uniform procedures, the EPA's Risk Assessment
Council (senior managers with significant respon-
sibilities for assessment and reduction of risks) di-
rected the Risk Assessment Forum to develop
ecological risk assessment guidelines using the same
open process that was used in developing human
health risk assessment guidelines. The first docu-
ment produced from the process, the "Framework
Report," is discussed below.
EPA'S FRAMEWORK FOR ECOLOGICAL
RISK ASSESSMENT
The "Framework Report" describes a basic and
flexible structure for conducting ecological risk as-
sessments within the EPA. The framework provides
principles not only for estimating risks from chem-
icals, but also for predicting impacts from non-
chemical stressors (e.g., habitat loss from human
activities), and retrospectively for assessing site-spe-
cific impacts. The "Framework Report" is neither
a procedural guide nor a regulatory requirement
within the EPA. Rather, it is intended to foster a
consistent approach to ecological risk assessment
within the EPA, identify key issues, and define
terminology.
The framework is conceptually, similar to the
approach used for human health risk assessment
but is distinctive in its emphasis in three areas.
First, ecological risk assessment can consider ef-
fects beyond those on individuals of a single spe-
cies and may examine a population, community, or
ecosystem. Second, there is no single set of ecolog-
ical values to be protected that can be generally ap-
plied. Rather, values are selected from a number of
possibilities based on both scientific and policy con-
siderations. Finally, there is an increasing aware-
ness of the need for ecological risk assessments to
consider nonchemical as well as chemical stressors.
The framework for ecological risk assessment is
illustrated in Figure 1. The risk assessment process
is shown within the bold line. Figure 1 also illus-
trates the interaction of risk assessment with data
acquisition, data verification, and monitoring. la
the "Framework Report," a distinction is made be-
tween data acquisition (which is outside the risk as-
sessment process) and data analysis (which is an
inteeral part of an ecological risk assessment). The
point in the risk assessment process. At that point,
the risk assessment stops, the necessary data are ac-
quired, then the assessment resumes. Verification
and monitoring can help determine the overall ef-
fectiveness of the framework approach, provide
necessary feedback concerning the need for future
modifications of the framework, help evaluate the
effectiveness and practicality of policy decisions,
and indicate the need for new or improved scien-
tific techniques [3].
Finally, whereas risk assessment and risk man-
agement are distinct processes, Figure 1 indicates
two points of interface between these two processes
during discussions between the risk assessor and
risk manager. At the initiation of the risk assess-
ment, the risk manager can help ensure that the risk
assessment will provide information relevant to
making decisions on the issues under consideration,
while the risk assessor can ensure that the risk as-
sessment addresses all relevant ecological concerns.
Effective communication is also important at the
end of the risk assessment process to provide the
risk manager with a full and complete understand-
ing of the assessment's conclusions, assumptions,
and limitations.
The remainder of this section discusses the three
major phases of ecological risk assessment shown
in Figure 1: problem formulation, analysis, and
risk characterization. To illustrate the described
concepts and issues, a simplistic, hypothetical ex-
ample concerning nutrient loads to an estuary will
also be discussed.
Problem formulation
Problem formulation is a planning and scoping
process that links the regulatory or management
goal to the risk assessment. Its end product is a
conceptual model that identifies the environmen-
tal values to be protected (the assessment end •
points), the data needed, and the analyses to be
The initial steps in problem formulation include
the identification and preliminary characterization
of the stressor, the ecosystem potentially at risk,
and the ecological effects. Performing this analy-
sis is an interactive process; foe example, gathering
information on the characteristics of a stressor
helps to define the ecosystems potentially at risk
from the stressor as well as the ecological effects
that may result. The ecosystem within whiebeffects
occur provides the ecological context for the assess-
ment. Knowledge of the ecosystem potentially at
risk can help identify ecological components (i.e.,
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1666
S.B. NORTON ET AL.
Discussion
between the
Risk Assessor
and
Risk Manager
(Planning)
Ecological Risk Assessment
PROBLEM FORMULATION
A
N
A
L
Y
S
Characterization
of
Exposure
Characterization
of
Ecological
Effects
RISK CHARACTERIZATION
o
09
5T
o
o
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a
o
3
3
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Framework for ecological risk assessment at EPA
1667
assessment end point. For example, a decline in a
sport fish population (the assessment end point)
may be evaluated by using laboratory studies on
the mortality of surrogate species such as the fat-
head minnow (the measurement end point).
Assessment and measurement end points may
involve ecological components from any level of
biological organization, ranging from individual
organism's to the ecosystem itself. In general, the
use of a suite of assessment and measurement end
points at different organizational levels can build
greater confidence in the conclusions of the risk as-
sessment and ensure that all important end points
are evaluated. In some situations, measurement
end points at one level of organization may be re-
lated to an assessment end point at a higher level.
For example, measurement end points at the indi-
vidual level (e.g., mortality, reproduction, and
growth) could be used in a model to predict .effects
on an assessment end point at the population level
(e.g., viability of a trout population in a stre'am).
Sound professional judgment is necessary for
proper assessment and measurement end point se-
lection, and it is important that both the selection
rationale and the linkages between measurement
end points, assessment end points, and policy goals
be clearly stated. More detailed discussions of end
points and selection criteria can be found in Suter
[4,5], Kelly and Harwell [6], U.S. Department of
the Interior [7], and EPA [8],
The initial evaluation of stressors, the ecosystem
potentially at risk, and ecological effects are inte-
grated with the end points to develop a conceptual
model for the assessment. The conceptual model
describes how the stressor might affect ecological
components of the natural environment [9]. For
example, the stressor may cause adverse effects by
interacting directly with an ecological component.
A stressor may also cause adverse effects indirectly,
for example, by affecting the food or habitat on
which the ecological component of interest depends.
The conceptual model includes a description of
possible exposure scenarios, which are qualitative
descriptions of how the various ecological compo-
nents co-occur with or contact the stressor. Each
scenario is defined in terms of the stressor, the type
of biological system and principal ecological com-
ponents, how the stressor will contact or interact
with the system, and the spatial and temporal
scales. Finally the conceptual model also describes
the approaches, analyses, and data needed to con-
duct the assessment.
Mthoueh there may be many ways that a stres-
considered most likely to contribute to risk are se-
lected for further evaluation in the analysis phase.
Professional judgment is needed to select the most
appropriate focus for the risk assessment, and it is
important to document the selection rationale.
In our hypothetical example, a risk manager
may be concerned about possible effects of nutri-
ent inputs to an estuary. During the problem for-
mulation phase, the ways that nutrient inputs may
cause effects in the estuary are described. For ex-
ample, nutrient loads may directly alter benthic
community structure; ultimately result in decreased
dissolved oxygen levels, which then may increase
mortality rates of fish or invertebrates; or reduce
aquatic vegetation abundance, which then may in-
directly affect wildlife and fish populations that de-
pend on the plants. Although several of these'
hypotheses may be evaluated further in the analy-
sis phase, for the purposes of this example we will
focus on effects to the aquatic vegetation. The as-
sessment end point may be the maintenance of the
abundance and distribution of several species of
aquatic vegetation as established by baseline mea-
surements. The measurement end points could be
growth and reproduction measurements of these
species in the laboratory and field.
Analysis
The analysis phase of ecological risk assessment
consists of the technical evaluation of the data on
the potential effects and exposure of the stressor.
The analysis phase is based on the conceptual
model developed during problem formulation. Al-
though this phase consists of characterization of
ecological effects and characterization of exposure,
the dotted line in Figure 1 illustrates that the two
are best performed interactively. An interaction be-
tween the two elements will ensure that the charac-
terized ecological effects are compatible with the
biota and exposure pathways identified in the ex-
posure characterization. The outputs of ecological
effects characterization and exposure characteriza-
tion are summary profiles that are used in the risk
characterization phase.
It is important to describe clearly and estimate
quantitatively the assumptions and uncertainties in-
volved in both analysis steps. In the majority of as-
sessments, data will not be available for all aspects
of these analyses, and those data that are available
may be of questionable or unknown quality. Typ-
ically, the assessor will have to rely on a number of
assumptions with varying degrees of uncertainty as-
sociated with each. These assumptions will be
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1668
S.B. NORTON ET AL.
inferences based on analogy with similar chemicals
and conditions, estimation techniques, and so
forth, all of which contribute to the overall uncer-
tainty. The uncertainties in these two steps are
brought forward and summarized during risk
characterization.
Characterization of exposure. The objective of
the exposure characterization is to combine the spa-
tial and temporal distributions of both the ecolog-
ical component and the stressor to evaluate the
co-occurrence or contact between the ecological
component and the stressor. The way exposure is
characterized will depend on the stressors being
evaluated and the assessment and measurement end
points. In the case of physical alterations of com-
munities and ecosystems, exposure can be broadly
expressed as co-occurrence, for example, the co-
occurrence of a wetland community with fill ma-
terial. Exposure analyses of individuals often focus
on actual contact with the stressor, as organisms
may not contact all of the stressor present in an
area. For chemical stressors, the analyses may fo-
cus further on the amount of chemical that is bio-
available, that is, available for uptake by the
organism. Some chemical exposure analyses also
follow the chemical within the organism's body and
estimate the amount that reaches the target organ.
In order to estimate exposure appropriately, the
temporal and spatial scale of the stressor distribu-
. tion must be compatible with that of the ecologi-
cal component. A temporal scale may encompass
the life span of a species, a particular life stage, or
a particular cycle, for example, the long-term suc-
cession of a forest community. A spatial scale may
encompass a forest, a lake, a watershed, or an en-
tire region. Stressor timing relative to organism life
stage and activity patterns can greatly influence the
occurrence of adverse effects. Even short-term
events may be significant if they coincide with crit-
ical life stages. Periods of reproductive activity may
be especially important, because early life stages are
often more sensitive to stressors and adults may
also be more vulnerable at this time.
The product of the characterization of exposure
is an exposure profile that quantifies the magnitude
and the spatial and temporal pattern of exposure
for the scenarios developed during problem formu-
lation. Exposure profiles can be expressed by using
a variety of units. For chemical stressors operating
at the organism level, the usual metric is expressed
in dose units, for example, milligrams per kilogram
body weight per day. For higher levels of organi-
zation, such as an entire ecosystem, exposure may
area per time. For physical disturbance, the expo-
sure profile may be expressed in other terms, such
as percentage of removed habitat or the extent of
flooding per year.
In our nutrient loading example, exposure may
be characterized by modeling or measuring the nu-
trient concentrations in the parts of the estuary
suitable for plant growth, say, in depths of 2 m or
less. The temporal and spatial variation would also
be addressed, and the uncertainty in the measure-
ments and model would be discussed.
Characterization of ecological effects. The re-
lationship between the stressor and the assessment
and measurement end points identified during
problem formulation is analyzed in characteriza-
tion of ecological effects. The analysis focuses on
describing the relationship between the amount of
stressor and the magnitude of ecological effects
elicited. Any extrapolations from measurement end
points to assessment end points are also conducted
during this phase. Finally, the evidence for a causal
relationship between the stressor and the measure-
ment and assessment end points is evaluated.
Data from both field observations and experi-
ments in controlled settings can be used to evalu-
ate ecological effects. Controlled laboratory and
field tests (e.g., mesocosms) can provide strong
causal evidence linking a stressor with a response
and can also help discriminate between multiple
stressors. Data from laboratory studies tend to be
less variable than those from field studies, but be-
cause environmental factors are controlled, re-
sponses may differ from 'those in the natural
environment. Observational field studies (e.g.,
comparison to reference sites) provide environmen-
tal realism that laboratory studies lack, although
the presence of multiple stressors and other con-
founding factors (e.g., habitat quality) in the nat-
ural environment can make attributing observed
effects to specific stressors difficult.
The test data are used to quantify the relation-
ship between the amount of the stressor and the
magnitude of the response, and to evaluate the
cause-effect relationship. Ideally, the stressor-
response evaluation quantifies the relationship be-
tween the stressor and the assessment end point.
When the assessment end point can be measured,
this analysis is straightforward. When it cannot be
measured, the relationship between the stressor and
measurement end point is established first, then ad-
ditional extrapolations, analyses, and assumptions
are used to predict or infer changes in the assess-
ment end point.
-------
Framework for ecological risk assessment at EPA
1669
between species, between responses, from labora-
tory to field, and from field to field. Differences in
responses among taxa depend on many factors, in-
cluding physiology, metabolism, resource utiliza-
tion, and life history. The relationship between
responses also depends on many factors/including
the mechanism of action and internal distribution
of the stressor within the organism. When extrap- -
olating between different laboratory and field set-
tings, important considerations include differences
in the physical environment and organism behav-
ior that will alter exposure, interactions with other
stressors, and interactions with other ecological
components.
In addition to these extrapolations, an evalua-
tion of indirect effects (e.g., effects on food, hab-
itat, or competing species), effects at other levels of
organization, effects at other temporal and spatial
scales, and recovery potential may be necessary.
Whether these analyses are required in a particular
risk assessment will depend on the assessment end
points identified during problem formulation. The
need for these types of analyses may also be iden-
tified during risk characterization after an initial
evaluation of risk.
Another important aspect of the characteriza-
tion of ecological effects is to evaluate the strength
of the causal association between the stressor and
the measurement and assessment end points. This
• information supports and complements the stressor-
response assessment and is of particular importance
when the stressor-response relationship is based on
field observations. An evaluation of causal evidence
augments the risk assessment and contributes to the
weight of evidence analysis supporting the judg-
ment that a causal relationship exists between the
stressor and response. Many of the concepts applied
in human epidemiology can be useful for evaluat-
ing observational field studies [10]. An example
of ecological causality analysis was provided by
Woodman and Cowling [11], who evaluated the
causal association between air pollutants and injury
to forests.
The results of the characterization of ecological
effects are summarized in a stressor-response pro-
file that describes the stressor-response relation-
ship, any extrapolations and additional analyses,
and evidence of causality (e.g., field effects data).
For practical reasons, the results of. the stressor-
response analysis are often summarized as one ref-
erence point, for instance, a 48-h LC50. Although
useful, such values provide no information about
rh^ <:lnne or shane of the stressor-resnonse curve.
points on the curve are identified, the difference in
magnitude of effect at different exposure levels can
be reflected in risk characterization.
In our example, the characterization of ecolog-
ical effects may include stressor-response curves
from the laboratory or field that relate the concen-
trations of nutrients to changes in growth and re-
production of the plant species of interest. Studies
of nutrient loads to other, similar estuaries and the
associated response of vegetation may also be sum-
marized. The uncertainties and assumptions (e.g.,
that response in the field is the same as that in the
laboratory) would also be summarized.
Risk characterization
Risk characterization is the final phase of eco-
logical risk assessment. The profiles of exposure and
ecological effects serve as input to risk character-
ization whenever risks are estimated and described.
In the first step of risk characterization, risks
are estimated by integrating the exposure and ef-
fects data to yield an expression of the likelihood
of adverse effects occurring as a result of exposure
to a certain stressor. Depending on the type of
data, the risk may be expressed in a qualitative or
quantitative fashion. The integration may be per-
formed by comparing single exposure and effect
values, by comparing distributions within the ex-
posure and effect profiles, or through the use of
simulation models. The nature of the data and the
requirements of the risk assessment will largely de-
termine which method or combination of methods
will be used. Another important activity associated
with this step is the discussion of uncertainties en-
countered during problem formulation, analysis
phase, and risk characterization. Uncertainties arise
due to data and knowledge gaps, and the assessor
must often use assumptions to bridge these gaps.
Although such assumptions are necessary, their use
often leads to uncertainty in tKe final assessment,
which has to be acknowledged through a qualita-
tive or quantitative uncertainty analysis.
After the risks and uncertainties have been es-
timated, the assessor summarizes the results and
discusses the overall confidence in the risk assess-
ment. This is achieved by objectively considering
the sufficiency of the data, evidence of the cause-
and-effect relationship, and any ancillary data in
a weight-of-evidence evaluation. The objective is to
describe the risk in terms of the assessment end
point identified in the problem formulation phase.
Without this crucial connection, the results of the
risk assessment mav not be readily apparent to the
-------
1670
S.B. NORTON ET AL.
portant, the assessor provides an interpretation of
the ecological significance of the identified risks.
Ecological significance may be described in terms
of the spatial and temporal extent of the effects; the
nature and magnitude of the effects; and, when
possible, an estimation of the recovery potential
once the stressor is removed. Depending on the ob-
jectives of the assessment, the risks may also be
placed in a broader ecological context by discuss-
ing the implications of the effects to other compo-
nents of the ecosystem.
The risk characterization phase of our nutrient
loading example would begin by integrating the nu-
trient levels measured or modeled in the estuary
with the stressor-response curves. These could be
integrated simply by comparing different points
along the stressor-response curves with the concen-
trations anticipated in the field. Alternatively, the
stressor-response and exposure information could
be integrated by using a model that would simulate
the changes in the plant community resulting from
the nutrient load. The uncertainties of the assess-
ment, including those from the problem and anal-
ysis phases, would be described, and the overall
confidence in the assessment would be discussed.
The nature, magnitude, and spatial and temporal
extent of effects would also be discussed. Finally,
the effects on the plant community might be placed
in a broader ecological context by discussing the
implications for the wildlife and fish that depend
on the plants.
Risk characterization forms the basis for a dis-
cussion of the results with the risk manager. The
risk assessment is used by the risk manager, along
with economic,, legal, and social concerns in the
risk management process, to evaluate management
options. A consideration of the basic principles of
ecological risk assessment will contribute to a final
product that is both credible and germane to the
needs of the risk manager.
The "Framework Report" is the first step in a
long-range effort to improve ecological risk assess-
ments within the EPA. In the short term, the basic
principles provided in the report are intended to
foster a consistent approach for terminology in and
conduct of risk assessments. The framework, as
part of a longer term guidelines development pro-
cess, will serve as a basis for identifying topics in
future guidelines activities.
FUTURE DEVELOPMENT OF EPA'S
ECOLOGICAL RISK ASSESSMENT GUIDELINES
The publication of the "Framework Report"
continuing the process into the second and third
phases of guidelines development.
The second phase of the strategy focuses on ac-
quiring information on a series of guidance-issue
areas that have been identified during several work-
shops as essential to the development of a guideline
[12,13]. The second phase of the program" will pro-
duce a series of resource reports, comprised of one
or more scientific white papers for each of the guid-
ance-issue areas. In addition, a suite of problem-
oriented case studies will be developed to illustrate
the application of the guidelines.
The third phase of the program involves the in-
tegration of the white papers, the problem-oriented
examples, and the "Framework Report" into the
first EPA-wide guidelines for ecological risk assess-
ment. The goal of this plan is to use the framework
as the platform for developing Agency ecological
risk assessment guidelines by integrating new sci-
entific information from each of the issue areas and
case studies.
Guidance-issue areas
It became apparent during the process of devel-
oping the "Framework Report" that there were a
number of important scientific issues for which
additional information and research would be
needed before ecological risk assessment guidelines
could be developed. The following are the types
of issues and needs that emerged from workshop
discussions:
Scale —the issues related to spatial, temporal,
and biological scale
Stressors —the need to define exposure for non-
chemical stressors and multiple stressors
End points — the importance of identifying the
ecosystems and selecting the end points po-
tentially at risk
Ecological effects—the need for information on
estimating direct, indirect, and cumulative ef-
fects across biological scales
Recovery—the importance of measuring the po-
tential for ecosystem recovery
Variability—the incorporation of estimates of
natural variability into assessments
Ecological significance—providing information
on the ecological significance of change
Uncertainty—treating uncertainty explicitly in
risk assessments
Causality—information on how to determine
causality in heterogeneously stressed envi-
ronments.
-------
Framework for ecological risk assessment at EPA
1671
lustrate the risk assessment process as applied to
various types of problems. Second was the need for
risk management guidance that both identifies the
context within which the risk assessment resides
and discusses the interfaces between risk assessment
and risk management.
Problem-oriented case studies
The strategy proposed for developing problem-
oriented case studies has two components: criteria
for selection and a format for the analysis of the
case studies. The review and analysis of each case
study will follow the three-phase process outlined
in the framework. Specific attention will be given
to assessing the applicability of the process outlined
in the framework document and to the guidance
areas discussed above. The case studies will also
provide valuable information on analytical ap-
proaches, methodologies, and the scientific feasi-
bility of conducting risk assessments for particular
problem settings.
The criteria used to select the case studies in-
clude basic organizing principles (stressor type,
level of ecological organization, ecosystem type,
and spatial and temporal scale), the guidance-issue
areas, and regulatory needs. For example, case
studies could be selected to illustrate toxic, non-
toxic chemical, and nonchemical stressors operat-
ing at widely different spatial and temporal scales
in different types of ecosystems.
The value of the case studies to the ecological
risk assessment guideline process can be viewed
from several perspectives:
Framework application—illustrate the applica-
tion of the description of the risk assessment
process to different types of problems
Evaluation—evaluate the adequacy of the prin-
• ciples and concepts used in the framework
Guidance areas-*-provide state-of-the-science in-
formation on specific guidance issues being
developed in parallel white papers
Feasibility-determine the scientific feasibility
(models, methods,- etc.) for conducting risk
assessments in a variety of problem settings
Agency needs—provide specific examples of
risk assessment to illustrate a spectrum of
Agency regulatory needs.
Preparation of ecological risk
assessment guidelines
The third phase of the proposed guideline pro-
gram will involve the preparation of ecological risk
guidelines by the EPA. The issue-ori-
become the primary resources for the Agency's eco-
logical risk assessment guideline work groups. The
result will be a series of ecological risk assessment
guidelines that will include descriptions of principles
and concepts, provide generic guidance, include is-
sue- and problem-oriented resource volumes, and
contain specific applications of the risk assessment
process.
The first document proposed for this phase is a
general ecological risk assessment guideline that
will provide specific guidance for the scientific is-
sues associated with conducting ecological risk as-
sessments as outlined in the "Framework Report."
This approach is consistent with the scientific com-
munity's recommendations to develop an initial
guideline that focuses on the scientific issues inte-
gral to the risk assessment process. The proposed
guideline, therefore, represents an expansion of the
principles and criteria developed in the framework.
The intention is that the ecological risk assessment
guideline provide detailed guidance and a range of
problem-oriented illustrations for each stage of the
risk assessment process. This approach assures that
the guideline will focus on the important scientific
issues and provide guidance on the use of the guide-
line for specific problems.
SUMMARY
Increased awareness of ecological issues has
emphasized the need for improved ecological risk
assessment methodology. The ecological risk as-
sessment process will evolve as new ideas and re-
search advancements improve our basic knowledge
about how ecological components interact and how
stressors alter such interactions.
The EPA has taken the first steps to develop
agencywide guidance for conducting ecological risk
assessments, continuing a long-standing-EPA pro-
gram to make the risk assessment process more sys-
tematic. Inevitably, it will be a multiyear effort,
just as it has been in the case of human health risk
assessment, where development of those guidelines
has been a product of several years of review and
discussion involving scientists and policy makers.
Publication of the "Framework Report" now
provides a structure on which future guidelines can
be built. The EPA, however, recognizes that eco-
logical risk assessment is a rapidly developing sci-
ence driven by a desire to expand our capabilities
beyond assessing single chemical effects on the re-
sponses of individual species. The framework has,
therefore, been designed to accommodate a variety
of stressors causins a diversity of ecological effects.
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1672
S.B. NORTON ET AL.
ing regional- and global-scale problems increases.
In its present form, the framework, through its dis-
cussion of basic principles, operational definitions,
and identification of key issues, will foster consis-
tency within the EPA and assist risk assessors in
avoiding errors of omission or in pursuing risk as-
sessment questions that cannot be applied in a reg-
ulatory context.
Over the next few years, more substantive guid-
ance will be issued. Although the exact format for
this guidance has not yet been decided, it will be an
expansion of the principles discussed in the frame-
work with emphasis being given to the specific el-
ements of the risk assessment process, as described
by the framework. Just as in the case of the human
health risk guidelines, these guidelines will not be
rigid and will encourage the use of professional
judgment within the framework of a logical and
scientifically sound structure. Development of risk
assessment guidelines has historically provided an
opportunity for discussion and debate among sci-
entists, policy makers, and the public as to what
society values and what level of protection is suf-
ficient. This will certainly be true in the case of the
ecological risk assessment guidelines, as the Agency
grapples with translating its broad regulatory man-
dates into concrete risk assessment policies and
objectives.
.Acknowledgement—Suzanne Marcy cochaired the work
group that developed early drafts of the "Framework Re-
port." Other members of the work group included Mi-
chael Brody, David Mauriello, Anne Sergeant, and Molly
Whitworth. Many peer reviewers, both inside and outside
the EPA, made valuable suggestions for revising the
"Framework Report." Especially noteworthy were the
participants in the May 1991 peer review workshop, which
was chaired by James Fava, with discussions led by Law-
rence Barnthouse, James Falco, Mark Harwell, and Ken-
neth Reckhow.
10
11
12
13
REFERENCES
1. U.S. Environmental Protection Agency. 1992 A
framework for ecological risk assessment EPA
630/R-92-001. Risk Assessment Forum, Washington,
2. U.S. Environmental Protection Agency. 1990. Re-
ducing risk: Setting priorities and strategies for en-
vironmental protection. SAB-EC-90-021. Science
Advisory Board, Washington, DC.
3. U.S. Environmental Protection Agency. 1992. Peer
review workshop report on a "Framework for ecolog-
ical risk assessment." EPA 625/3-91-022. Risk Assess-
ment Forum, Washington, DC.
t. Suter, G.W. II. 1990. Endpoints for regional ecolog-
ical risk assessments. Environ. Manage. 14:9-23
>. Suter, G.W. II. 1989. Ecological endpoints. In W
Warren-Hicks, B.R. Parkhurst and S.S. Baker, Jr.,
eds., Ecological Assessments of Hazardous Waste
Sites: A Field and Laboratory Reference Document
EPA 600/3-89-013. U.S. Environmental Protection
Agency, Corvallis, OR, pp. 2-1-2-28.
i. Kelly, J.R. and M.A. Harwell. 1990. Indicators of
ecosystem recovery. Environ. Manage. 14:527-546
. U.S. Department of Interior. 1987. Injury to fish and
wildlife species. Type B Technical Information Doc-
ument. CERCLA 301 Project. Washington, DC.
. U.S. Environmental Protection Agency. 1990. Eco-
logical indicators. EPA 600/3-90-060. Environmental
Monitoring and Assessment Program, Washington,
. National Research Council. 1983. Risk assessment in
the federal government: Managing the process. Na-
tional Academy Press, Washington, DC.
. Hill, A.B. 1965. The environment and disease: Asso-
ciation or causation? Proc. R. Soc. Med. 58:295-300.
. Woodman, J.N. and E.B. Cowling. 1987. Airborne
chemicals and forest health. Environ. Sci. Techno/
21:120-126.
U.S. Environmental Protection Agency. 1992. Report
on the ecological risk assessment guidelines strategic
planning workshop. EPA 630/R-92-002. Risk Assess-
ment Forum, Washington, DC.
U.S. Environmental Protection Agency. 1991. Sum-
mary report on issues in ecological risk assessment.
EPA 625/3-91-018. Risk Assessment Forum, Wash-
ington, DC.
-------
Susan v**i azouez-Tutt
Susan Velasquez-Tutt received her Bachelor of Sciehoe degree
in Life sciences from the Massachusetts Institute of Technology in
W84. She currently worfcs for the U.S. EPA in the Environmental
Criteria and Assessment office in Cincinnati, Ohio and is pursuing
her doctoral degree in toxicology at the University of Cincinnati.
Her thesis research is an investigation into the activation of the
H-ras oncogene in liver tumors induced in male mice by chlorination
by-products in drinKing water. other areas of general interest
include mechanisms of carcinogenesis and related risK assessment
issues,
-------
Combination of Cancer Data in Quantitative Risk Assessments;
Case Studies of Perchloroethylene and Bromodichloromethane
Sarah T. Vater1
Patricia M. McGinnis1
William S. Stiteler1
Susan F. Velazquez-Tutt2
Linda A. Knauf2
Rita S. Schoeny2
1 Syracuse Research Corporation
2159 Gilbert Avenue
Cincinnati, Ohio 45206
Environmental Criteria and Assessment Office
U.S. Environmental Protection Agency
26 W. Martin Luther King Jr. Drive
Cincinnati, Ohio 45268
To be presented at:
"Conference on the Risk Assessment Paradigm After Ten Years:
Policy and Practice Then, Now, and in the Future"
April 5-8, 1993
Wright-Patterson Air Force Base
Dayton, Ohio
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ABSTRACT
There are often . several data sets that may be used in
developing a quantitative risk estimate for a carcinogen. The
decision, however, is usually made to base this estimate on the
dose-response data for tumor incidences from a single
sex/strain/species of animal. When appropriate, the use of more
data should result in a higher level of confidence in the risk
estimate. The decision to use more than one data set
(representing, for example, different animal sexes, strains,
species or tumor sites) can be made following biological and
statistical analyses of the compatibility of these data sets.
Biological analysis involves consideration of factors such as the
relevance of the animal models, study design and execution, dose
selection and route of administration, the mechanism of action of
the agent, its pharmacokinetics, any species- and/or sex-specific
effects, and tumor site specificity. If the biological analysis
does not prohibit combining data sets, the' statistical
compatibility is then investigated. The generalized likelihood
ratio method is proposed for determining the compatibility of
different data sets with respect to a'common dose-response model,
such as the linearized multistage model. The biological and
statistical factors influencing the decision to combine data sets
are described, followed by case-studies of perchloroethylene and
bromodichloromethane.
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INTRODUCTION
The estimation of the carcinogenic hazard posed to humans by
a chemical involves a great deal of scientific judgement.
*
Uncertainty is inherent to cancer risk assessments, particularly
those developed from animal data, because assumptions must be made
in areas for which data are scarce. These include, for example,
appropriate transformations for extrapolation of dose from test
animals to humans and for high-to-low doses, and the assumption
that the same biological process(es) leading to cancer in
laboratory animals are operative in humans as well. Statistical
uncertainty is also inherent in the use of sample data to derive
inferences about a large population.
Oftentimes, more than one statistically significant positive
tumorigenic response is observed for carcinogens, either in both
sexes in the same bioassay, or in a different strain or species
from a separate study. The U.S. EPA, however, most frequently
bases carcinogen risk assessments on results of a bioassay from a
single sex/strain/species of animal (Stiteler and Vater, 1989).
The 1986 Guidelines for Cancer Risk Assessment (U.S. EPA, 1986a)
suggest that the data set used in estimate quantitative cancer risk
.should be determined by the relevance of the animal model to human
health risk, the quality of the data and the apparent sensitivity
of the chosen animal model. In many cases, however, it is not
known whether one animal model is more appropriate for
extrapolation to humans than another. When the differences in
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animal sensitivities are small (as measured by the quantitative
estimates derived from the data), or when the estimates incorporate
large uncertainties, it could be argued that a more reasonable
approach would be to derive a risk estimate using more of the
available data. A combination of data may result in a higher level
of associated confidence or it might result in a risk assessment
with improved statistical properties.
While the use of more of the available information is a
worthwhile objective, it is complicated by both biological and
statistical issues. The available carcinogenicity data for a
specific chemical rarely originate from replicate studies; rather,
they are derived from studies using different sexes, strains,
and/or species of animals in which the responses depend on a
variety of biological factors. Differences in study design and
execution may further complicate the issue of combining data from
different sources.
This work was initiated to develop an approach in which more
of the available data on carcinogenicity might be used to derive a
quantitative risk estimate, as well as to determine when such an
approach would be biologically and statistically appropriate.
T» r-nMBTNTNG riPPTWOCmilCTTY INFORMATION
There are several methods that could be applied to utilize
more of the available carcinogenicity information: the choice of
a risk estimate'derived from a single data set, with additional
3
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risk estimates from other data sets being used as corroboration for
the chosen value; the use of some average value (e.g., a median or
geometric mean) of risk estimates derived from different data sets;
or the combination of individual data sets, prior to the
calculation of a risk estimate. - The term "data set" is defined
here as the tumor incidence data for a single anatomical site or
combination of sites within a single sex/strain/species of animal
(e.g., lung adenomas and carcinomas in female F344 rats). The
definition of an adequate data set includes the criterion of
statistically significant increased tumor incidence in exposed
groups by comparison to controls or by a trend analysis.
If it is assumed that the data sets represent samples from the
same population, then statistically, the preferred method is to
combine them prior to derivation of the quantitative estimate of
cancer potency. This is especially true when the estimate is
expressed as an upper 95% confidence limit on the slope in the low-
dose region of the animal dose-response curve, based on quantal
data utilizing a linearized multistage procedure. As is the case
for any confidence limit, this estimate reflects both the natural
variability and the size of the data set. In general, when all
other factors are held constant, a smaller data set yields a larger
value of the upper confidence limit than a larger data set because
of the uncertainty inherent in basing the estimate on a limited
amount of information. Thus, the average of two or more
quantitative estimates each based on a small data set necessarily
incorporates this sample uncertainty. Alternatively, the
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combination of data sets prior to determining a quantitative
estimate would decrease this uncertainty and result in a better
defined upper confidence level.
This approach requires that the responses being considered for
combination be carefully evaluated with respect to their biological
similarities and differences to judge whether a combination of the
data is appropriate and would improve the overall risk assessment.
Examination of the biological basis for combination is used in
conjunction with a statistical test to evaluate the compatibility
of two or more data sets with the same multistage model, that is,
whether the data sets can be assumed to represent samples from the
same population. The statistical analysis consists of a likelihood
ratio test based on information obtained from the GLOBAL86 version
(Howe et al., 1986) of the linearized multistage procedure.
BIOLOGICAT. CRITERIA FOR COMPTNTNG CARCINOGENTCITY DATA
The criteria for determining the biologic compatibility of 2
data sets are discussed here as issues pertaining to study quality
or to the mechanism of action of the carcinogen. Study quality
factors include elements determined by the design of the study
itself which may affect the biological processes involved in
carcinogenesis: These include study design factors (e.g., dosing
regimen and vehicle, duration of exposure) and study quality
factors (e.g., purity of test compound, number of animals and
adequate survival). Mechanistic questions are those pertaining
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directly to the assessment of how the chemical induces cancer, such
as genotoxicity, species, strain or sex differences in
pharmacokinetics which may affect the production of carcinogenic
metabolites, or organ-specific responses to the chemical.
Figures 1 and 2 present these considerations in the form of
decision trees that can be used in the determination of whether to
combine data. Several qualitative indicators of differential
sensitivity to a carcinogen (e.g., differences in latency, degree
of malignancy) may also be important. Benign 'tumors with the
potential to progress to malignancy are usually considered as
equivalent to malignant tumors for purposes of quantitation. While
benign and malignant tumors are frequently combined within data
sets, the decision to combine them across data sets may depend on
the tumor type and the assumed pattern of progression. A
significant difference in tumor latency might be considered
evidence that one sex, strain or species is substantially more
sensitive than another to the effects of-a carcinogen.
Several of these biological factors are considered in more
detail in' the" case studies that follow the discussion of the
statistical methodology. Other biological issues not discussed in
these case studies may be pertinent in the evaluation of other
chemicals.
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Study Quality
Yes
Route of exposure
same for all data sets?
No
Yes -«
Dose factors affecting
assessment of dosen-esponse
in one or more data sets?"
Assess magnitude of
dose factor differences
Evidence for same systemic ^
effects by multiple routes? I
No
[JJ°J
DNC-
Other study quality
issues?"*
Will combining compensate
for study deficiencies? J
ryi
Go to "Mechanism
of Action"
Do Not Combine .
«.g.,Dose rate (continuous vs. non-continuous); equivalent duration of
exposure; MTD reached/exceeded
.umber/size rf*»»g^ps;
Figure 1. Decision tree used to analyze issues of study quality in combining cardnogenicity
risk assessment. These issues must be jcons.dered in
regarding the mechanism of action of the carcinogen.
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Mechanism of Action
Yes
Species - (sex, strain) - specific
mechanism in any data set?
No
Chemical is gene-toxic?^}—^-
Yes
T
Evidence for other similar A
^mechanism across data setsTy
„
Evidence for target organ
toxicity/cel! proliferation
in one data set (not others)?^/
I
Do PB-PK models allow>
target site dosimetry?
Evidence for saturation of relevant
metabolic pathways in one data set?
Yes
No
T
Is there tumor site concordance
across data sets?
f
( Are tumors induced
at multiple sites within
\^ sex'strain/species?^
I
I
(larget Site determined by:
- chemical-specific factors
- different kinetic/metabolic
pathways
- other host/organ-specific
Vjactprs ,
'Do PB-PK models
support similarities
across data sets?,
Overall assessment of mechanistic similarity
Yes
*e.g., peroxiisome proliferation
Figure 2. Decision tree used to analyze mechanistic similarity in carcinogenicity
data sets to be considered for combination.
8
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DATA
CARCINOGENICITY
The
statistical test proposed here is based on generalized
likelihood ratio theory and will be illustrated in the context of
the linearized multistage procedure. The use of the maximum
likelihood method for estimating unknown population parameter
related to the generalized likelihood ratio test for testing
likelihood estimation of parameter
s is
hypotheses
thus, maximum
estimates for a
to
single data set will be presented here prior
testing the compatibility of two data sets with the
discussions
same linearized multistage model
DOSE-PKSPOWSE MODELS
T.TKELIHOnn KSTIMATI™
in a cancer bioassay, where tumor development is considered to
be a dichotomous response, the number of exposed animals developing
cancer could be expressed by the binomial probability distribution:
(1)
fix) =
for x = 0,1,2, ••• ,N
where N is the number of animals tested, X is the number with'
tumors, and P is the probability of developing cancer. The
variables N and X are established from the bioassay data, while the
parameter P is unknown. Substituting the sample values of X and N
from the bioassay into Equation 1, the value of P that maximizes
the equation can be estimated. This value of P represents the
" 9
-------
highest probability of observing that particular value of X. From
this equation, the probability of observing any number of animals
with cancer can be computed, so that Equation 1 also represents a
likelihood function for the parameter P. For an experiment having
k groups (including a control group and 2 or 3 dose groups) then,
the overall likelihood function can be expressed as:
(2)
For a given bioassay, the data points X. and N; that are input
into Equation 2 represent the total number of animals with tumors,
and the number tested in the ith group, respectively. In a well-
reported study, however, individual animal data are available,
indicating a positive'or negative carcinogenic response for each
animal. Given the dichotomous nature of the response, and assuming
the animals respond independently, the likelihood function is then
expressed as a product of Bernoulli distributions, which, except
for the exclusion of the combinatoric term, is equivalent to the
binomial-based likelihood:
L =
(3)
10
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The likelihood function expressed in Equation 3 is simply a
product of terms - one
for each animal in the experiment. Each
tumor-bearing
animal contributes a quantity P, to the likelihood
is the
and each nonresponder contributes a term (1-P,-) , where p.
probability (unknown) of developing cancer for the ith group to
which the
animal belongs. Looking at this problem with
the
Bernoulli distributions, then, means
we
can specify the likelihood
solution to
function for combined data sets from two separate studies by
combining the individual likelihood functions. The numerical
the value of the P, terms is equivalent for both the
Binomial and Bernoulli problems.
The problem remains, then, of how the dose of the carcinogen
affects the likelihood function. This may be expressed by assuming
the data follow a particular dose-response model. This has the
effect of establishing .a relationship between the P,
tricting them to functions that include the measure of dose.
To illustrate, we can assume the dose-response model to be a
-stage model with no background rate:
terms,
res
one
P(d) = 1 - exp(-6d) ,
(4)
where 6 is an unknown model parameter to be estimated from the
data. For simplicity, we can also assume that the higher of the
two doses is one unit (d2=l) and that the lower dose is one-half
that amount (d^O.5). Then, the probabilities of responding for
each of the two groups are:
11
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! = l - exp(-6 dj.) = 1 - exp(-0.5 9) ,
and
P2 = 1 - exp(-6
- exp(-9) .
Solving these two equations for the common value 9 gives:
= -2ln(lt- Pi) ,
and
9 = -
- P7
These are set equal and solved to give:
P2 = 1 - (1 - Pt)
(5)
(6)
(7)
(8)
(9)
The assumption of a one-stage model results in a restriction
of the maximum of the likelihood function to those values
satisfying the equation, P2 = 1 - (1 - P.,)2. All possible one-stage
models are represented by this equation. The maximum of the
likelihood function under this constraint occurs when P1 = 0.14 and
P2 = 0.26 (in contrast a perfect fit would yield P1 = P2 = 0.20).
The value of the model parameter, 9, corresponding to (P1,P2) =
(0.14, 0.26) is 0.30, the maximum likelihood estimate of 8.
A STATISTICAL TEST OF COMPATIBILITY OF DATA SETS
' 12
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. if two or more data sets are judged to be biologically
suitable for derivation of a quantitative estimate, the next step
is to determine if they are compatible with a common dose-response
model, (i.e., the same linearized multistage model). This
determination could be done by testing the null hypothesis:
H0: The data sets are compatible with a common model,
#
against the alternative hypothesis,
H,: The data sets are not compatible with a common model.
To test the null hypothesis, the method of maximum likelihood,
described above, can be used to estimate the model parameters. If
two or more data sets are combined, the resulting likelihood
function would be equal to the product of the likelihood functions
for the individual data sets:
LT -
(10)
If each of the individual terms is maximized, then it follows
that the product (i.e., the overall likelihood function) is
maximized. The joint likelihood LT can be maximized under either
of two assumptions: it can be assumed that the two data sets can
be fit to a common dose-response model (H0) ; or it can be assumed
that the two data sets fit different dose-response models (H,) .
13
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In order to test HQ, a generalized likelihood ratio test can
be conducted by comparing the ratio of the two maxima under the two
assumptions:
A =
maxL(ff0)
maxL(#0 U
(ID
Under HQ, -2 In A has an asymptotic chi-square distribution
(Lindgren, 1976; Cox and Hinkley, 1974). The degrees of freedom
for the test are determined by the constraints on the parameter
space, and in this case are equal to 1. The test, then, is
performed by comparing -21n A = 2[max In L(HQ U H.,) - max In .L(HQ) ]
with the tabulated chi-square at a chosen level of significance.
Alternatively, we could use the chi-square distribution to
determine the probability (p-value) of seeing a larger value of
-2In A than we have calculated.
We would reject HQ, i.e., reject that the data sets are
compatible with a common model and accept H1, if there is a
significant difference (p-value < chosen significance level)
between the values of the joint likelihood functions.when different
models are used for the two data sets and when a common model is
used. In contrast, we would accept H0, i.e., accept that the two
data sets are compatible with a common model and reject H1, if
there is little difference (p-value > chosen significance level)
between the maxima of the joint likelihood functions under these
two assumptions.
14
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CASE STUTW l!
Background
Tetrachloroethylene (Perchloroethylene, PCE) is a volatile
chlorinated solvent which has been widely used as a dry cleaning
agent and in industrial metal degreasing operations. PCE has been
reported to produce increased incidence cancer in rats and mice.
The data sets from these studies appeared to present possibilities
for combination both because of their apparent quantitative
similarities (McKone and Bogen, 1992) and the fact that, in the .
mouse, the same tumor site was observed in both sexes by different
routes of administration. In addition, numerous investigations
into the pharmacokinetics and mechanisms of action of the compound
have been reported. The availability of this mechanistic animal
data, as well as data on the pharmacokinetic behavior of PCE in
humans, afforded the opportunity to examine a number of biological
issues in more detail than is possible for most chemicals. The
analysis for PCE provides an example of how certain types of
information can be applied in the process of determining whether
data sets with positive responses may be appropriate for
combination.
Discussion of Data Sets
in the NCI (1977) study, groups of Osborne-Mendel rats and
B6C3F1 mice (5o' animals/sex/dose group) were administered gavage
doses of PCE (> 99% pure) in corn oil, 5 days/week for 78 weeks.
15
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Vehicle-treated and untreated control groups consisted of so
animals/sex/species. During the final 26 weeks, treatment was
administered to rats in a pattern of 1 week without treatment
followed by 4 weeks with treatment. Rats and mice were observed
for. an • additional 32 and 12 weeks, respectively, following
treatment. Time-weighted average doses in (mg/kg)/day for the rats
were 471 and 941 for males and 474 and 949 for females. The
corresponding doses for the mice are shown in Table 1. For the
mice, metabolized doses were calculated (U.S. EPA, 1985) based on
estimates of total urinary metabolites, derived using the
relationship described by Buben and O'Flaherty (1985). Because it
is widely accepted that the biological effects of PCE are dependent
upon its metabolism' to more reactive species, the carcinogenic
response is assumed to be related to the metabolized dose.
In the NCI (1977) study, no treatment-related increases in
neoplastic lesions were reported in rats, however this may have
been affected by decreased survival in the high-dose animals.
Median survival times were 66 and 44 weeks in high-dose females and
males, respectively, versus >88 weeks in controls. In mice,
significantly increased incidences of hepatocellular carcinoma were
observed in mice of both sexes at both dose levels,' as compared
with their respective controls. The tumor incidences are shown in
Table 1.
An inhalation bioassay in F344/n rats and B6C3F1 mice was
conducted by NTP (1986). Groups of 50 animals/sex were exposed to
PCE (99.9% pure) 6 hours/day, 5 days/week for 103 weeks, at levels
16
-------
of 0, 200 and 400 ppm (rats) or 0, 100 and 200 ppm (mice). In
rats, the incidence of mononuclear cell leukemia was significantly
increased in rats of both sexes at both exposure levels: 28/50,
37/50, 37/50 in males; 18/50, 30/50, 29/50 in females, for
controls, 16w and high concentrations, respectively. Renal tubular
cell adenomas and carcinomas (combined), a rare tumor type, also
occurred in male rats with a statistically significant positive
trend (1/49, 3/49, 4/50). In mice, significantly increased
incidences of hepatocellular carcinoma alone or combined
adenoma/carcinoma were observed in treated animals of both sexes at
both exposure levels. The tumor incidences in mice are shown in
Table 2. For the inhalation study, estimates'of metabolized dose
were based on levels of urinary metabolites measured over a 72,hour
period following a single six-hour inhalation exposure (Schumann et
al., 1980; U.S. EPA, 1986b).
Additional studies which have been judged inadequate by the
U.S. EPA or which were not positive have been reviewed elsewhere
(U.S. EPA, 1985).
Several data sets (consisting of anatomical sites with
statistically'significantly increased tumor incidence or a positive
trend in tumor incidence) exist that could potentially be used in
the derivation of a quantitative risk estimate: hepatocellular
17
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carcinoma in male and/or female mice from gavage administration
(NCI, 1977); hepatocellular carcinomas alone or combined with
adenomas in male and/or female mice from inhalation exposure (NTP,
1986) ; mononuclear cell leukemia in male and/or female rats exposed
by inhalation (NTP, 1986); and renal tubular cell adenomas and
carcinomas in male rats exposed by inhalation (NTP, 1986).
The decision tree shown in Figures 1 and 2 may be used as a
guideline for the analysis of biological issues which are relevant
to the question of whether to combine data sets for PCE. The first
question to be considered is that of the route of exposure of the
chemical. While the NCI (1977) and NTP (1986) administered PCE by
gavage and inhalation,, respectively, the systemic effects in mice
were similar by both routes (hepatocellular carcinoma). There was
no indication of route-specificity of metabolism or effects,
probably in part because PCE is not highly reactive and has a
relatively long half-life in the body. The NCI (1977) study did
use a shorter exposure period (78 weeks, vs. 2 years in NTP, 1986) ,
which introduces uncertainty into a quantitative combination of the
data sets. Since mice and rats in the NCI (1977) study were
observed for up to 90 and 110 weeks, respectively, the study
durations were similar. Estimates of lifetime average daily
metabolized dose (over the duration of the study) have been
calculated based on estimates of urinary metabolites of PCE (U.S.
EPA, 1985 and 1986b). These estimates, shown in Tables 1 and 2,
adjust in part for the differences between the exposure durations
and the dosing, regimens in the NCI (1977) and the NTP (1986)
20
-------
studies. In this particular case, the question of whether a
combination of data across routes really presents advantages for
the risk assessment must also be addressed. Route-specific risk
estimates, for oral and inhalation exposure, may be preferred,
since human exposure by both routes occurs.
Since numerous studies have focused on the biochemical actions
of PCE and related compounds, a more detailed examination of the
mechanisms contributing to tumor development is possible than is
the case for most chemicals. Referring to the Mechanism of Action
section of the decision tree (Figure 2), the initial question to be
considered is whether any of the data sets represent species- (or
strain or sex) specific responses. It has been suggested that the
kidney tumors observed in the in the male rat in the NTP (1986)
study may have been due at least in part to the accumulation of the
protein a-2M-globulin following administration of PCE (Green et
al., 1990), a response which is specific to the male rat (reviewed
in U.S. EPA, 1991). Subchronic inhalation studies with PCE at
exposure concentrations of 1000 ppm showed evidence of the hyaline
droplet nephropathy which results from accumulation of a-2/i-
globulin (Green et al., 1990). However, direct histological
evidence for this mechanism at the doses used in the NTP (1986)
study is lacking. Other hypotheses for the induction of the renal
tumors involve renal hydrolysis of glutathione conjugates to
mutagenic metabolites (Green, 1990). and regenerative hyperplasia
associated with recurrent cytotoxicity. Support for the latter
hypothesis includes evidence of nephrotoxicity in rats in both the
21
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NCI (1977) and NTP (1986) studies. Whatever the actual mechanism
or combination of mechanisms, the probability of species and organ
specificity suggests that the renal tumors should not be used in
combination with other data sets for the quantitative risk
assessment.
*,
The biological significance of the mononuclear ,cell leukemia
(MCL) in rats of both sexes has also been questioned. MCL is a
common tumor in aging rats, with a high and variable spontaneous
incidence. In the NTP (1986) study, the incidence of these tumors
in concurrent controls (56% in males; 36% in females) was unusually
high compared to historical chamber controls at the performing
laboratory (47% in males; 29% in females), which raises the
questions regarding the increased incidence observed in the study.
The relevance to humans of this tumor type has also been debated.
Thus, while the PCE-induced MCL incidence may be indicative of a
carcinogenic response in the rat, the use of these tumors in
combination with others in the rat or the mouse may not be
appropriate.
The use of liver tumor incidence in B6C3F1 mice for human
health risk assessment has been questioned because of the genetic
susceptibility of this strain of mice to liver tumor induction.
.The high spontaneous incidence is believed to be due in part to a
genetic predisposition associated with a locus termed hcs (Hanigan,
1990) ; in male mice this predisposition may be compounded by
hormonal influences (Haseman et al., 1985). However, when liver
tumors are increased in mice of both sexes, and tumors are seen at
22
-------
other sites or in other species, as is the case for PCE, it is less
likely that the response can be attributed solely to a sex/strain-
specific mechanism. Therefore, data sets based on the mouse liver
would not be excluded from combination at this point based on a
strain or sex-specific mechanism.
The next mechanistic question to be considered is that.of the
genotoxicity of PCE. The evidence for genotoxicity of PCE, briefly
summarized here, is equivocal (reviewed in detail in U.S. EPA,
1985). Results from mutagenicity assays, in the presence or
absence of metabolic activation systems, have been mostly negative.
Studies reporting positive results were conducted at cytotoxic
concentrations and showed weak responses. In addition,
mutagenicity assays conducted with highly purified PCE showed
negative results. However, metabolites formed via a glutathione
conjugation pathway may be potential mutagens and have not been
thoroughly investigated.
Sufficient data are available to demonstrate significant
differences between mice and rats in the pharmacokinetics and
metabolism of PCE. Several studies have shown that oxidative
metabolism of PCE reaches saturation at relatively low
concentrations in the rat (> 100 ppm) , relative to the mouse
(reviewed in U.S. EPA, 1985; Bolt, 1987). Thus, at higher exposure
levels, mice generate higher levels of PCE metabolites than do
rats. ' Trichloroacetic acid, a major metabolite of PCE, is a potent
peroxisome-inducing agent, and it has been suggested that hepatic
peroxisome proliferation might be a causative factor in the genesis
23
-------
of the mouse liver tumors (Odum et al., 1988). The inability of
rats to generate sufficient levels of TCA from PCE exposure (due to
saturation of the oxidative pathway) would explain why liver tumors
are not seen in rats, although a causal relationship between
peroxisome proliferation and tumor induction has not been clearly
established.
Overall, the lack of evidence of genotoxicity and the
demonstrated differences in metabolism across species supports the
suggestion that the two types of tumors observed in the rat and the
liver tumors in the mice arise by different mechanisms and should
not be combined for the calculation of a quantitative estimate.
For purposes of combination, the available data sets appear to
consist of hepatocellular tumors in mice of both sexes, by either
the oral or inhalation route. Data sets from male and female mice
were modeled separately for the gavage and inhalation studies
(Tables 1 and 2), using the linearized multistage procedure.
Results of the likelihood ratio tests for the male and female mouse
combined data sets .(from both the oral and inhalation routes) are
shown in Table 3. The p-value for the test of the null hypothesis
of compatibility with a common model is <0.05 for each combination
of data sets, indicating rejection of that hypothesis. 'Figure 3
depicts the plots of the multistage models fit by Global86 for the
hepatocellular carcinoma incidence in male and female mice, and for
the combined data sets, from the NCI (1977) study. Similar plots
for the hepatocellular adenoma and/or carcinoma in individual and
combined data sets from the NTP (1986) study are shown in.Figure 4.
24
-------
in both cases, the differing shapes of the dose-response curves for
the males and females are evident, suggesting that sex-specific
differences in tumorigenesis are of sufficient magnitude to
preclude combining the data sets. Thus, the appropriate basis for
a quantitative risk estimate for PCE remains a single data set.
25
-------
TABLE 3
Likelihood Ratio Tests for
Perchloroethylene - Induced Mouse Liver Tumors
Study
NCI, 1977
(Gavage)
NTP,1986
(Inhalation)
NTP, 1986
(Inhalation)
Data Sets
cf ca1
9 ca
cf + 9 cal
cf ca
9 ca
cf + 9 ca
cf ad/ca2
9 ad/ca
cf + 9 ad/ca
3
2
6
3
1
5
q.,* p value
.4E-1
.5E-1
. 6E-1
.5E-1
<0.01
. 1E-1
.OE-2
<0.01
Compatible?
' No
No
No
'carcinoma only
2combined adenoma" and/or carcinoma
26
-------
FIGURE 3
1,00
(1)
(2)
10 30 30
DOSE
O
5
O
a
in
u
a
o
u
-------
FIGURE 4
Dose-Response Curves for NTP (1986) for Mouse Liver
Adenomas/Carcinomas: Males (1); Females (2); and Comi>ined(3)
CD
z
5
z
o
a
en
ui
D;
Z
o
a
L_
(D
Z
a
o
a
(fi
u
a
z
o
o
c
a
L.
CD
Z
a
o
a
w
u
a
z
o
o
a:
»TP iimmtini srmv: frmf msr IM» mxal
(D
(2)
(3)
10 12 U 16 18 M 2?
28
-------
CASE STUDY 2; BROMODTHH-LOROMETHANE
Background
Bromodichloromethane (BDCM) is a volatile trihalomethane formed
when chlorine interacts with organic constituents in water. BDCM
has been detected in many untreated waters as well as drinking
water systems treated by chlorination (reviewed in U.S. EPA, 1992).
Several studies have suggested a possible association between
cancer incidence, particularly of bladder, colon and rectal
cancers, with water chlorination (U.S. EPA, 1992 and 1993). There
are no epidemiological studies of BDCM intake alone, and the intake
of chlorinated water involves exposure "to a mixture of compounds,
including the trihalomethanes.
Several animal'studies have attempted to evaluate whether chronic
oral exposure to BDCM can induce tumors. Carcinogenicity studies
wherein BDCM was administered in the drinking water (Tumasonis et
al., 1987; Voronin et al., 1987) or diet-supplemented in
microcapsules (Tobe et al., 1982) did not induce a statistically
elevated tumor incidence in the treated Wistar rats or CBAxC57Bl/6
mice than in the respective controls. These studies have
limitations such as incomplete histological examination, .lack of
discussion regarding solubility of BDCM in water at high doses and'
volatilization. These studies have been reviewed in U.S. EPA
(1992). NTP (1987) administered BDCM by gavage in corn oil to
F344/N rats and B6C3F1 mice of both sexes and reported tumors at
multiple sites. Several researchers have suggested that the
difference in findings between the NTP and other studies- may be due
" 29
-------
to different strains of rats, the administration of a bolus dose
vs. continuous dosing and/or an interaction of the chemical with
the corn oil vehicle. The latter possibility has received much
attention with chloroform (Jorgenson et al., 1985; Bull et al.,
1986).
The choice of data showing a statistically significant positive
tumorigenic response that can be used for a quantitative risk
estimate are limited to those from the NTP (1987) bioassay.
Discussion of data sets
*
NTP (1987) administered 0, 50 or 100 mg/kg/day BDCM (99% pure) in
corn oil to F344/N rats (50/sex/dose) by gavage for 102 weeks.
Groups of 50 B6C3F1 mice received 0, 25 or 50 mg/kg/day (males) or
0, 75 or 150 mg/kg/day (females) BDCM by gavage in corn oil for 102
weeks. Controls received the vehicle. The study in male rats was
restarted due to decreased survival of the vehicle controls after
10.5 months due to excessive room temperature. Several sites
showing a,statistically significant increased tumor incidence in
treated groups relative to controls or a statistically significant
dose-related increase in incidence (positive linear trend) were
reported: large intestine adenomatous polyp/adenocarcinoma and
kidney tubular cell adenoma/adenocarcinoma in both sexes of rats,
hepatocellular adenomas/adenocarcinomas in female mice, and kidney
tubular cell adenoma/adenocarcinomas in male mice. The tumor
incidences for these sites are shown in Tables 4-8. The historical
vehicle control, incidence for large intestine tumors and renal
30
-------
tubular cell tumors in male F344/N rats is 0.2% (3/1390) and 0.6%
(8/1448) , respectively, and in female rats of this strain is 0%
(0/1400) and 0.1% (2/1447), respectively (NTP, 1987). In male
B6C3F1 mice, the historical vehicle control incidence of renal
tubular cell tumors is 0.3% (5/1490). The vehicle historical
control incidence of hepatocellular adenomas/carcinomas in female
B6C3F1 mice is 7.8% (116/1489) (NTP, 1987). The low historical
control incidence for the tumor types seen at statistically
significant increased incidence in this study indicate these tumors
are uncommon and biologically important, thereby constituting data
sets adequate for calculation of a risk estimate (U.S. EPA, 1986a).
Since all the adequate data sets are from the same bioassay,
issues related to study quality are minimal. The route of exposure
for both sexes of both species was oral gavage at similar dose
rates and dose volumes for lifetime of the animals. Appropriate
controls were employed, complete histology was performed, and, with
the exception of the female mice, survival was comparable amongst
treated and' control groups. While survival in all groups of the
female mice was decreased due to ovarian abscesses, these data are
not compromised for use in quantitative risk estimation as
appropriate statistical analysis may adjust for survival. The MTD
31
-------
TABLE 4
Bromodichloromethane Large Intestine Tumor Incidence
in F344/N Rats1
Human
Equivalent
Dose2 Adenomatous '
(m'g/kg) /day Polyp
Male Female
0.0 0/50 0/46
5.8 - 0/50
6.8 3/49
10.9 - " 7/47
13.0 33/50
Adeno-
carcinoma
Male
0/50
-
11/49
- .
3'8/50
Female
0/46
0/50
-
6/47
. -
Combined polyp
and carcinoma
Male Female
0/50 ' 0/46
0/50
13/49
12/47
45/50
1Adapted from NTP, 1987
2Based on surface area adjustment'
32
-------
TABLE 5
Bromodichloromethane Renal Tumor Incidence in F344/N Rats1
Human
Equivalent
Dose2 Tubular Cell
(mg/kg)/d adenoma
Tubular Cell
carcinoma
Combined
adenoma
carcinoma
Male Female
Male
Female
Male
Female
0.0
5.8
6.8
10.9
13.0
0/50
1/49
3/50
0/50
1/50
6/50
0/50 0/50 0/50 0/50
0/50 - V50
0/49 - V49
9/50 ~ 15/50
10/50 - 13/50
1 Adapted from NTP, 1987
2Based on surface area adjustment
33
-------
TABLE 6
Bromodichloromethane Large Intestine and/or Renal
Tumor Incidence in F344/N Rats
Human
Equivalent
Dose2
(mg/kg)/day
0.0
5.8
6.8
10.9
13.0
Males "
0/50
13/49
46/50
Females
0/46
1/50
24/48
.
1 Adapted from NTP, 1987
2Based on surface area adjustment
34
-------
TABLE 7
Bromodichloromethane Liver Tumor Incidence in B6C3F1 Female Mice1
Human
Equivalent
Dose2
Hepatocellular
adenoma
adenoma &
carcinoma
0.0
4.2
8.1
1/50
13/48
23/50
2/50
5/48
10/50
Combined
carcinoma
3/50
18/48
29/50
'Adapted from NTP, 1987
2Based on surface area adjustment
35
-------
TABLE 8
Bromodichloromethane Renal Tumor Incidence in B6C3F1 Male Mice1
Human
Equivalent
Dose2
(mg/kg)/day
0.0
1.5
3.0
Tubular Cell
adenoma
1/46
2/49
6/50
Tubular Cell
carcinoma
0/46
0/49
4/50
Combined adenoma
and carcinoma
1/46
2/49
9/50
1Adapted from NTP, 1987
2Based on surface area adjustment
36
-------
was reached in rats at the high dose as evidenced by decreased body
weight and liver and kidney lesions. Similarly, histological
findings in the kidney, liver, thyroid and testis of the low dose
male mice and decreased body weight and thyroid hyperplasia in high
dose female mice suggest the MTD was reached.
Concerns about the use of corn oil as a vehicle in studies of
trihalomethanes have been raised (Withey et al., 1983; Jorgenson et
al., 1985). As such, the Science Advisory Board of the U.S. EPA
recommended that the female mouse hepatocellular carcinomas not be
used in quantitative risk assessment (SAB, 1992).
There is a paucity of data on the metabolism and
pharmacokinetics of BDCM, limiting comparisons of mechanism of
action among sexes or species of animals. Much of the information
for BDCM has been inferred from data on a more studied
trihalomethane, chloroform. In vivo and in vitro studies with the
trihalomethanes demonstrate that there are two primary routes of
metabolism, oxidative and reductive (reviewed in U.S. EPA, 1992).
Thorton-Manning et al. (1993) recently demonstrated that P-450
enzymes are involved in the metabolism of BDCM. The research of
Mink et al. (1986) suggested that BDCM may be more rapidly absorbed
and metabolized and/or more extensively metabolized'in mice than
rats after a single dose.
•BDCM is considered by the U.S. EPA to have genotoxic potential
(U.S. EPA, 1992).' Conflicting results in vivo and in vitro test
systems have been attributed to inadequacies or variation in
experimental protocols, such as difficulty in achieving sufficient
37
-------
exposure to volatile BDCM and different metabolic capability of
various cell types (NTP, 1987; reviewed in U.S. EPA, 1992). Based
on the premise that BDCM is most likely to act by a genotoxic
mechanism, and that similar genetic processes form the basis for
carcinogenicity, all data sets, with the exception of female mouse
liver tumors, thus far qualify as the basis for a quantitative
estimate of risk.
Renal tubular hyperplasia was reported in both sexes of rats.
In addition, renal cytomegaly was noted in male rats and male mice
at the high dose levels (NTP, 1987). These findings suggest that
an epigenetic mechanism, such as regenerative hyperplasia, may also
play a role in carcinogenic activity of BDCM at this site, although
the data are. insufficient to establish such a role. Reitz et al.
(1982) suggested this mechanism for chloroform carcinogenesis.
The NTP reported that hyaline droplet formation in the kidney was
not seen in the male rats, therefore, involvement of a-2ju-globulin
nephrbpathy in the formation of these tumors appears unlikely.
Hyperplasia was not reported in the large intestine of either sex
or species (NTP, 1987). '
It is difficult to assess the role differential toxicokinetics
among the sexes or species may have on the mechanism . of
carcinogenicity of BDCM for these data sets. Differences in
. absorption (e.g., residence time in the gastrointestinal tract) or
metabolic rates and/or pathways may account for the observation of
large intestine tumors in rats but not in mice, and in the
observation of renal tumors in male mice and hepatocellular tumors
38
-------
in female mice. The interaction of the vehicle (corn oil) with
BDCM may also be a determinant in site-specificity/sensitivity,
although the Science Advisory Board of the U.S. EPA did not
consider a vehicle effect likely in the development of the renal or
large intestine tumors (SAB, 1952).' without definitive biological
data, there do not appear to be mechanistically different processes
occurring at the different sites or between the different sexes or
species that would discourage combining across the
sites/sexes/species. Therefore, possibilities for calculation of
a quantitative risk estimate are: male rat large intestine/kidney
tumors, female rat large intestine/kidney tumors and large
intestine/kidney tumors in both sexes combined.
The linearized multistage procedure was used to model these
data sets and the likelihood ratio test applied to evaluate the
compatibility of the combined data set of both sexes as described
above. Table 9 shows that the data sets for male and female rats
are not statistically compatible with the same multistage model.
These results suggest that some of the biological factors
influencing the dose-response relationship are not evident from the
available information. " Reexamination of the large intestine and
renal tumor incidence shows that large intestine tumors occur at.
the low dose in the male rats but not in the female rats. Thus,
the large intestine/kidney tumor" incidence in the males is
comprised primarily of large intestine tumors, whereas, in the
females kidney and large intestine tumors contribute equally to the
combined incidence. This could implicate such factors as
39
-------
TABLE 9
Bromodichloromethane Likelihood Ratio Tests
Data Sets
tf Mouse renal
9 Rat renal
d1 Rat renal
9 Rat Gl/renal
cf Rat Gl/renal
cf + 9 Rat Gl/renal
cf + 9 Rat renal
*
q.
6.2E-2
9.5E-3
8.5E-3
l.OE-2
2.5E-2.
6.3E-3
p value . Compatible?
•
<0.001 No
0.067 Yes
cf + 9 Rat renal and
cf Mouse renal
<0.. 001
No
40
-------
differential metabolic pathways (e.g., oxidative in the kidney and
reductive in the intestine) in. the disparate responses observed
across sexes, although further research is needed to clarify the
underlying biological basis.
The observation of renal tubular cell adenomas and adenocarcinomas
in both sexes of rats and the male mice may have biological
relevance and tends to support the hypothesis that a similar
mechanism of action may be operating at this site. However, the
renal tumors in mice occurred at lower dose levels than the tumors
in rats, suggesting the mice may be "more sensitive" than the rats.
Moore et al. (1993) showed that nephrotoxicity occurs to a greater
extent in mice than in rats when BDCM is administered
subchronically in drinking water. The most conservative option for
quantitative estimate is the use of only the male mice kidney
tumors (Table 9). Biological similarity of the tumor type argues
in favor of combining data sets across both sexes of rats and
possibly across both rats and mice. The latter options have the
advantage of increasing the number of dose groups to five and
seven, respectively. The results, as presented in Table 9, show
that the male and female rat kidney tumor data are compatible with
the same multistage model. When these combined data are tested
with the male mice kidney data, the likelihood ratio test indicates
these data sets are not compatible with the same multistage model.
in summary, the risk assessor is left with various options to
estimate the carcinogenic risk to BDCM. The most conservative
option is the use of the male mouse kidney data alone. Use of the
41
-------
large intestine/kidney tumors for each sex of rat and kidney tumors
for male and female rats combined are two other options which are
both biologically and statistically viable. Use of each tumor site
in each sex of rat alone provide four other options. The slope
factors for these eight data sets are shown in Table 9. All of
these quantitative estimates are within an order of magnitude.
Biological information has provided little to resolve which data
set is most appropriate, leaving the decision to the judgement of
the assessor.
DISCUSSION
Our confidence in quantitative cancer risk assessments may be
increased by the use of as many of the available data as possible.
Case studies on perchloroethylene and bromodichloromethane are used
to illustrate many of the biological and statistical issues that
must be considered in combining multiple data sets used to
calculate a cancer risk estimate. In the case of PCE, bioassays in
mice and rats were available by the oral route and by inhalation.
Several data sets demonstrating a statistically significantly
increased tumor incidence were available: hepatocellular tumors in
male and female mice by both routes of administration; mononuclear
cell leukemia in both sexes of rats by inhalation; and kidney
tumors in male rats by inhalation. An investigation into biologic
issues, however, reveals that the mechanisms by which these tumors
are likely to arise are sufficiently different to preclude the
combination of data sets, except for the liver tumors in male and
42
_
-------
female mice. Subsequent statistical analysis of the mouse data
sets for both the gavage and inhalation studies indicated that the
data from the two sexes could not be combined. In the case of
bromodichloromethane, only one NTP bioassay was available for
quantitative analysis. In this-experiment, BDCM was administered.
by gavage to rats and mice, with a positive tumor response being
observed for the large intestine and kidney of male and female
rats, liver tumors in female mice, and kidney tumors in male mice.
Study design issues (i.e., use of corn oil gavage) precluded use of
the liver tumors. The remaining data sets seemed plausible
candidates for combination, and statistical compatibility was
subsequently analyzed. While the kidney tumor incidences in male
and female rats were found to be compatible with the same
multistage model, the resulting potency estimate was less
conservative than that generated from the male mouse kidney data
alone. Based on these findings, options for risk management of
BDCM are presented. The final decision, as with all risk
assessments, will involve the use of assumptions and scientific
j udgement.
43
-------
REFERENCES
H.M. Bolt, "Pharmacoki.netic Factors and Their Implication in the
Induction of Mouse Liver Tumors by Halogenated Hydrocarbons," Arch.
Toxicol., Suppl. 10, 190-203 (1987).
J.R. Buben, E.J. O'Flaherty, "Delineation of the Role of Metabolism
in the Hepatotoxicity of Trichloroethylene and Perchloroethylene:
.A Dose-Effect Study," Toxicology and Applied Pharmacology
78, 105-122 (1985).
B.J. Bull, J.M. Brown, E.A. Meierhenry, T.A. Jorgenson, M.
Robinson, and J.A. Stober, "Enhancement of the Hepatotoxicity of
Chloroform in B6C3F1 Mice by Corn Oil: Implications for chloroform
Carcinogenesis," Environmental Health Perspectives 69, 49-58
(1986).
Cox, D.R. Cox and D.V. Hinkley, Theoretical Statistics. Chapman and
Hall, New York, NY (1974).
T. Green, "Chloroethylenes: A Mechanistic Approach to
Human Risk Evaluation," Annual Review of Pharmacology and
Toxicology 30, 73-89 (1990).
T.Green, J. Odum, and J.K. Foster, "Perchloroethylene-Induced Rat
Kidney Tumors: An Investigation of the Mechanisms Involved and
Their Relevance to Humans," Toxicology and Applied Pharmacology
103, 77-89 (1990).
M.H. Hanigan, M.C. Winkler and N.R. Drinkwater, "Partial
hepatectomy is a promoter of hepatocarcinogenesis in C57BL/6J male
mice but not in C3H/HeJ male mice," Carcinogenesis, 11, 589-594
(1990).
J.K. Haseman, J.E. Huff, G.N. Rao, J.E. Arnold, G.A. Boorman and
E.E. McConnell, "Neoplasms observed in untreated and corn oil
gavage control groups of F344/N rats and (C57BL/6N x C3H/HeN)Fl
(B6C3F1) mice," J. Natl. Cancer Inst., 75, 975-984 (1985).
R.B. Howe, K.S. Crump, and C. Van Landingham, "GLOBAL86. A
computer program to extrapolate quantal animal toxicity data to low
doses," K.S. Crump & Company, Ruston LA (1986).
T.A. Jorgenson, E.F. Meierhenry, C.J. Rushbrook, R.J. Bull, and M.
Robinson, "Carcinogenicity of Chloroform in Drinking Water to Male
Osborne-Mendel Rats and Female B6C3F1 Mice," Fundamental and
Applied Toxicology 5, 760-769 (1985).
B.W. Lindgren, Statistical Theory. 3rd Ed.. MacMillan Publishing
Co.," Inc. New York, NY (1976).
44
-------
T.E. McKone and K.T. Bogen, "Uncertainties in Health-Risk
Assessment: An Integrated Case Study Based on Tetrachloroethylene
in California Ground Water," Regulatory Toxicology and Pharmacology
15, 86-103 (1992).
P.L. Mink, J. Brown, and J. Rickabaugh, "Absorption, Distribution
and Excretion of 14C-trihalomethanes in Mice and Rats," Bulletin of
Environmental Contamination and Toxicology 37, 752-758 (1986).
T.C. Moore, A.B. DeAngelo, and R.A\ Pegram, "Subchronic Toxicity of
Broraodichloromethane and Bromoform Administered to Mice and Rats in
Drinking Water," The Toxicologist 13(1), 359, (Abstract 1405)
(1993) .
National Cancer Institute (NCI), "Bioassay of Tetrachloroethylene
for Possible Carcinogenicity", U.S. Department of Health,
Education, and Welfare Publication No. (NIH) 77-813 (1977).
National Toxicology Program, "Toxicology and Carcinogenesis Studies
of Tribromomethane (Bromoform) in F344/N Rats and B6C3F1 Mice
(Gavage Studies)," (U.S. Department of Health and Human Services,
Research Triangle Park, N.C., Technical Report Series No. 321,
1987) .
National Toxicology Program, "Toxicology and Carcinogenesis of
Tetrachloroethylene (Perchloroethylene in F344/N Rats and B6C3F1
Mice (Inhalation Studies)," NIH Publication No. 86-2567, NTP TR
311, (1986).
J. Odum., T. Green, J.R. Foster, and P.M. Hext, "The role of
trichloroacetic acid and peroxisome proliferation in the
differences in carcinogenicity of perchloroethylene in the mouse
and rat," Toxicology and Applied Pharmacology 92, 103-112 (1988).
R.H. Reitz, T.R. Fox, and J.F. Quast, "Mechanistic Considerations
for Carcinogenic Risk Estimation," Chloroform Environmental Heath
Perspective 46, 163-168, (1982).
SAB. (Science Advisory Board) , Drinking Water Committee, "Review of
Health Criteria Document for Trihalomethanes by the Drinking water-
Committee of the Science Advisory Board." (U.S. Environmental
Protection Agency Science Advisory Board, Washington, D.C., 1992)
A.M. Schumann, T.F. Quast, P.G. Watanabe, "The Pharmacokinetics of
Perchloroethylene in Mice and Rats as Related to Oncogenicity,
Toxicology and Applied Pharmacology 55, 207-219 (1980).
W. Stiteler, and S. Vater, "Improved Methods for Combination of
Quantitative Risk Estimates for Carcinogens", Prepared by Syracuse
Research Corporation, Syracuse, NY under Contract No. 68-C8-0004
for the Office of Health and Environmental Assessment,
Environmental Criteria and Assessment Office, Cincinnati, OH.
45
-------
(1989).
J.R. Thorton-Manning, P. Gao, and P.O. Lilly, "Acute
Bromodichloromethane Toxicity in Rats Pretreated With Cytochrome
P450 Inducers and Inhibitors," The Toxicologist 13(1), 361
(Abstract 1412) (1993).
M. Tobe, Y. Suzuki, K. Aida, H. Yoshimoto, K. Takada, J. Kadoma, K.
Kobayashi, Y. Vehida, K. Anda, Y. Nakaji, Y. Suzuki, and M. Sato,
"Studies on the Chronic Oral Toxicity of Tnbromomethane,
Dibromochloromethane and Bromodichloromethane," (Unpublished
Intraagency Report to the National Institute of Hygienic Sciences,
Tokyo, Japan, Tokyo Medical and Dental University, (1982).
C.F. Tumasonis, D.N. McMartin, and B. Bush, "Lifetime Toxicity of
Chloroform and Bromodichloromethane When Administered Over a
Lifetime In Rats," Ecotoxicology and Environmental Safety 9, 233-
240 (1985).
U.S. Environmental Protection Agency, "Health Assessment Document
for Tetrachloroethylene (Perchloroethylene)", Prepared by the
Office of Health and Environmental Assessment, Environmental
Criteria and Assessment Office, Research Triangle Park, NC. EPA
600/8-82-005F (1985).
U.S. Environmental Protection Agency, "Guidelines for Carcinogen
Risk Assessment," Federal Register 51(185) 33992-34003 (1986a).
U.S. Environmental Protection Agency, "Addendum to the Health
Assessment Document for Tetrachloroethylene (Perchloroethylene)
(Prepared by the Office of Health Environmental Assessment,
Washington, D.C., EPA/600/8-82/005FA. Available from: National
Technical Information Service, Springfield, VA; PB-86-174489/AS),
(1986b).
U.S. Environmental Protection Agency, "Alphas-Globulin: Association
With Chemically Induced Renal Toxicity and Neoplasia in the Male
Rat." Prepared for the Risk Assessment Forum, U.S. Environmental
Protection Agency, Washington D.C. (1991).
U.S. Environmental Protection Agency, "Drinking Water Criteria
Document for Trihalomethanes. Revised External Review Draft,"
Office of Science and Technology, Washington, D.C., (1992).
U.S. Environmental Protection Agency, "Integrated Risk Information
System,.Online, Office of Health and Environmental Assessment,
Environmental Criteria and Assessment Office, Cincinnati, Ohio,
(1993).
W.M. Voronin, A.I. Donchenko, and A.A. Korolev, "Experimental Study
of the Carcinogenicity of Dichlorobromomethane and
46
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Dibroraochloromethane Which Are Formed During the Water Chlorination
Process," Gigiena i Sanitariia (Eng. Trans.) 19-21 (1987).
W.M. Voronin and N.N. Litvirov, "Carcinogenicity of Chloroform in
the Mouse," Voprosy onkolog 33(8), 81-85 (1987).
J.R. Withey, B.T. Collins, P.G. Collins, "Effect of Vehicles on the
Pharmacokinetics and Uptake of Four Halogenated Hydrocarbons From
the Gastrointestinal Tract of the Rat," Journal of Applied
Toxicology 3, 249-253 (1983).
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•fc OS. GOVERNMENT PWHTWO OFFICE: 1983-751-979
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